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1
Li, Ying. "Forecasting Long Term Highway Staffing Requirements for State Transportation Agencies." UKnowledge, 2016. http://uknowledge.uky.edu/ce_etds/42.
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The transportation system is vital to the nation’s economic growth and stability, as it provides mobility for commuters while supporting the United States’ ability to compete in an increasingly competitive global economy. State Transportation Agencies across the country continue to face many challenges to repair and enhance highway infrastructure to meet the rapid increasing transportation needs. One of these challenges is maintaining an adequate and efficient agency staff. In order to effectively plan for future staffing levels, State Transportation Agencies need a method for forecasting long term staffing requirements. However, current methods in use cannot function without well-defined projects and therefore making long term forecasts is difficult.
This dissertation seeks to develop a dynamic model which captures the feedback mechanisms within the system that determines highway staffing requirements. The system dynamics modeling methodology was used to build the forecasting model. The formal model was based on dynamic hypotheses derived from literature review and interviews with transportation experts. Both qualitative and quantitative data from literature, federal and state database were used to support the values and equations in the model. The model integrates State Transportation Agencies’ strategic plans, funding situations and workforce management strategies while determining future workforce requirements, and will hopefully fill the absence of long-term staffing level forecasting tools at State Transportation Agencies.
By performing sensitivity simulations and statistical screening on possible drivers of the system behavior, the dynamic impacts of desired highway pavement performance level, availability of road fund and bridge fund on the required numbers of Engineers and Technicians throughout a 25-year simulation period were closely examined. Staffing strategies such as recruiting options (in-house vs. consultants) and hiring levels (entry level vs. senior level) were tested.
Finally the model was calibrated using input data specific to Kentucky to simulate an expected retirement wave and search for solutions to address temporary staffing shortage.
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Quinn, Niall. "Forecasting of ocean state in a complex estuarine environment : the Solent-Southampton Water Estuarine System." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/359671/.
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Coastal flooding is a natural hazard causing devastation to many regions throughout the world, induced by the coincidence of high spring tides, large storm surges and waves. To reduce the risk posed by coastal inundation, warning systems have been developed to enable preparations to an expected threat. Although current operational predictions provide invaluable warnings, uncertainty in model formulations and input datasets, can lead to errors in forecasts. In order to provide coastal managers with the best possible information with which to make decisions, recent research has begun to focus on the movement from deterministic to probabilistic forecasting, which aims to explicitly account for uncertainty in the system. This research described the implementation of a regional tide-surge-wave model for the Solent-Southampton Water estuarine system, a region that is likely to experience increased risk of coastal flooding in the coming century. The accuracy of the model predictions were examined relative to in-situ measurements and those obtained from independent systems. Using the model, sources of error were examined and their effects upon the model predictions quantified, with particular reference made to the spatial variability throughout the region. In light of recent research, a probabilistic modelling approach, utilising a Monte Carlo technique used to provide a forecast capable of representing the uncertainty in the system, within a suitable time-frame for real-time flood forecasting that included an hourly Kalman filter data assimilation update. The findings presented in this thesis will be of interest to coastal modellers working in complex estuarine environments where the influences of tide-surge-wave interactions upon model predictions are uncertain. Furthermore, the application of a computationally efficient model, presented here, will provide a useful comparison with traditional physically-based systems to those wishing to quantify uncertainty in regions where computational resources are low
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Steed, Chad A. "Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis." Diss., Mississippi State : Mississippi State University, 2008. http://library.msstate.edu/etd/show.asp?etd=etd-10252008-080937.
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Mansoor, Shaheer. "System Surveillance." Thesis, Linköpings universitet, Statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-98189.
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In recent years, trade activity in stock markets has increased substantially. This is mainly attributed to the development of powerful computers and intranets connecting traders to markets across the globe. The trades have to be carried out almost instantaneously and the systems in place that handle trades are burdened with millions of transactions a day, several thousand a minute. With increasing transactions the time to execute a single trade increases, and this can be seen as an impact on the performance. There is a need to model the performance of these systems and provide forecasts to give a heads up on when a system is expected to be overwhelmed by transactions. This was done in this study, in cooperation with Cinnober Financial Technologies, a firm which provides trading solutions to stock markets. To ensure that the models developed weren‟t biased, the dataset was cleansed, i.e. operational and other transactions were removed, and only valid trade transactions remained. For this purpose, a descriptive analysis of time series along with change point detection and LOESS regression were used. State space model with Kalman Filtering was further used to develop a time varying coefficient model for the performance, and this model was applied to make forecasts. Wavelets were also used to produce forecasts, and besides this high pass filters were used to identify low performance regions. The State space model performed very well to capture the overall trend in performance and produced reliable forecasts. This can be ascribed to the property of Kalman Filter to handle noisy data well. Wavelets on the other hand didn‟t produce reliable forecasts but were more efficient in detecting regions of low performance.
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Paramygin, Vladimir A. "Towards a real-time 24/7 storm surge, inundation and 3-D baroclinic circulation forecasting system for the state of Florida." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024729.
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Chou, Shuo-Ju. "A conceptual methodology for assessing acquisition requirements robustness against technology uncertainties." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39467.
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The lack of system capability, budget, and schedule robustness against technology performance and development uncertainties has led to major setbacks in recent acquisition programs. This lack of robustness stems from the fact that immature technologies have uncertainties in their expected performance and development times and costs that translate to variations in system effectiveness and program development budget and schedule requirements. As such, the objective of this thesis is to formulate an assessment process that better informs acquisition decision-makers of program requirements robustness against such uncertainties.
To meet the stated research objective, a conceptual methodology for assessing acquisition requirements robustness against technology performance and development uncertainties was formulated. This general approach provides a structured process for integrating probabilistic and quantitative forecasting, multi-criteria decision-making, and decision-support techniques to generate the statistical data needed to quantitatively predict requirements robustness. The results of the robustness assessment indicates to the decision-makers whether or not the technology or set of technologies being developed for the program will result in system capabilities and program budget and schedule that meet decision-maker requirements and preferences. This results in a more informed and justifiable selection of program technologies during initial program definition as well as formulation of program development and risk management strategies.
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Lewe, Jung-Ho. "An Integrated Decision-Making Framework for Transportation Architectures: Application to Aviation Systems Design." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-04132005-204114/unrestricted/Jung-Ho%5FLewe%5F200505%5Fphd.pdf.
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Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2005.Amy R. Pritchett, Committee Member ; Moore, Mark D., Committee Member ; Wilhite, Alan, Committee Member ; Schrage, Daniel P., Committee Chair ; Mavris, Dimitri N., Committee Co-Chair ; DeLaurentis, Daniel A., Committee Member. Vita. Includes bibliographical references.
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Key, Peter Bernard. "Bayesian forecasting with state space models." Thesis, Royal Holloway, University of London, 1986. http://repository.royalholloway.ac.uk/items/87d86ed9-b2e7-4393-9fef-696f8c0cd147/1/.
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This thesis explores the use of State-Space models in Time Series Analysis and Forecasting, with particular reference to the Dynamic Linear Model (DLM) introduced by Harrison and Stevens. Concepts from Control Theory are employed, especially those of observability, controllability and filtering, together with Bayesian inference and classical forecasting methodology. First, properties of state-space models which depart from the usual Gaussian assumptions are examined, and the predictive consequences of such models are developed. These models can lead to new phenomena, for example it is shown that for a wide class of models which have a suitably defined steady evolution the usual properties of classical steady models (such as exponentially weighted moving averages) do not apply. Secondly, by considering the forecast functions, equivalence theorems are proved for DLMs in the steady state and stationary Box-Jenkins models. These theorems are then extended to include both time-varying and non-stationary models thus establishing a very general predictor equivalence. However it is shown that intuitively appealing DLMs which have diagonal covariance matrices are restricted by only covering part of the equivalent stability / invertibility region, and examples are given to illustrate these points. Thirdly, some problems of inference involving state-space models are looked at, and new approaches outlined. A class of collapsing procedures based upon a distance measure between posterior components is introduced. This allows the use of non-normal errors or Harrison-Stevens Class II models by condensing the normal-mixture posterior distribution to prevent an explosion of information with time, and avoids some of the problems of the Harrison-Stevens solution. Finally, some examples are given to illustrate the way in which some of these models and collapsing procedures might be used in practice.
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Fischer, Ulrike. "Forecasting in Database Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-133281.
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Time series forecasting is a fundamental prerequisite for decision-making processes and crucial in a number of domains such as production planning and energy load balancing. In the past, forecasting was often performed by statistical experts in dedicated software environments outside of current database systems. However, forecasts are increasingly required by non-expert users or have to be computed fully automatically without any human intervention. Furthermore, we can observe an ever increasing data volume and the need for accurate and timely forecasts over large multi-dimensional data sets. As most data subject to analysis is stored in database management systems, a rising trend addresses the integration of forecasting inside a DBMS. Yet, many existing approaches follow a black-box style and try to keep changes to the database system as minimal as possible. While such approaches are more general and easier to realize, they miss significant opportunities for improved performance and usability.
In this thesis, we introduce a novel approach that seamlessly integrates time series forecasting into a traditional database management system. In contrast to flash-back queries that allow a view on the data in the past, we have developed a Flash-Forward Database System (F2DB) that provides a view on the data in the future. It supports a new query type - a forecast query - that enables forecasting of time series data and is automatically and transparently processed by the core engine of an existing DBMS. We discuss necessary extensions to the parser, optimizer, and executor of a traditional DBMS. We furthermore introduce various optimization techniques for three different types of forecast queries: ad-hoc queries, recurring queries, and continuous queries. First, we ease the expensive model creation step of ad-hoc forecast queries by reducing the amount of processed data with traditional sampling techniques. Second, we decrease the runtime of recurring forecast queries by materializing models in a specialized index structure. However, a large number of time series as well as high model creation and maintenance costs require a careful selection of such models. Therefore, we propose a model configuration advisor that determines a set of forecast models for a given query workload and multi-dimensional data set. Finally, we extend forecast queries with continuous aspects allowing an application to register a query once at our system. As new time series values arrive, we send notifications to the application based on predefined time and accuracy constraints. All of our optimization approaches intend to increase the efficiency of forecast queries while ensuring high forecast accuracy.
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Lawson, Richard. "Adaptive state-space forecasting of gas demand." Thesis, Lancaster University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358799.
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Каткова, Тетяна Ігорівна. "Моделі і методи оцінки, прогнозування та управління стратегічною діяльністю підприємства в умовах невизначеності." Thesis, НТУ "ХПІ", 2017. http://repository.kpi.kharkov.ua/handle/KhPI-Press/35129.
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Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.03 – системи та процеси керування – Національний технічний університет "Харківський політехнічний інститут", Харків 2018.
Дисертаційну роботу присвячено вирішенню важливої та актуальної проблеми наукового обґрунтування і розробки комплексу моделей і методів оцінки, прогнозування та управління стратегічною діяльністю підприємства в умовах невизначеності. Розроблено моделі та методи оцінки і прогнозування стану об'єктів в умовах невизначеності з великим числом можливих станів і великим числом нечітко заданих факторів. Сформульовано і реалізовано концепцію системного стратегічного фінансового планування, що забезпечує комплексне рішення приватних задач стратегічного фінансового планування та управління станом підприємства з урахуванням їх взаємозалежності і взаємозв'язку. Запропоновано економіко-математичні моделі вибору стратегічних напрямків діяльності підприємства, що дозволило врахувати відмінності в рентабельності, рівнях ризику, розмірах розміщеного капіталу. Розроблено моделі та методи управління розподілом активів підприємства по стратегічних напрямках діяльності для кожної зі стадій багатокрокового управління інвестиційним портфелем підприємства з урахуванням відмінностей їх рентабельності та рівня ризику. Обґрунтовано комплекс математичних моделей і методів системного вирішення сукупності оптимізаційних задач вибору проекту плану матеріально-технічного розвитку з урахуванням обсягу вкладених коштів, рівня позикових коштів і леверидж-ефекту, що виникає при цьому. Розроблено моделі та методи розв'язання задач управління інвестиційним портфелем, що враховують невизначеність і ризик при оцінюванні стану зовнішнього середовища, а також рівня можливого прибутку від діяльності підприємства. Розглянуто та удосконалено моделі динаміки вартості активів в умовах ризику і невизначеності. Запропоновано математичну модель марківської динаміки вартості у марківському зовнішньому середовищі.Thesis for the degree of Doctor of Engineering in specialty 05.13.03 – systems and management processes. – National Technical University "Kharkov Polytechnic Institute", Kharkov, 2018. The thesis is devoted to the solution of an important and actual problem of the scientific substantiation and development of a complex of models and methods for assessing, forecasting and managing the strategic activity of an enterprise under uncertainty. Models and methods for estimating and predicting the state of objects under conditions of uncertainty with a large number of possible states and a large number of fuzzy factors are developed. The concept of strategic financial planning has been formulated and implemented, providing a comprehensive solution of particular problems of strategic financial planning and management of the enterprise condition taking into account their interdependence and interconnection. Economic and mathematical models for choosing strategic directions of the enterprise's activities were proposed, which allowed taking into account differences in profitability, risk levels, and the size of the allocated capital. The models and methods of managing the distribution of the company's assets by strategic lines of activity for each of the stages of multi-step management of the enterprise's investment portfolio, taking into account the differences in their profitability and the level of risk are developed. The complex of mathematical models and methods of the system solution of a set of optimization tasks for the selection of the draft plan for material and technical development is substantiated, taking into account the amount of funds invested, the level of borrowed funds and the resulting leverage effect. Models and methods for solving investment portfolio management problems have been developed, taking into account uncertainty and risk in assessing the state of the external environment, as well as the level of possible profit from the activities of the enterprise. Models of the dynamics of the value of assets under risk and uncertainty are reviewed and improved. A mathematical model of the Markovian value dynamics in Markov's environment is proposed.
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Burden, Lindsay Ivey. "Forecasting earthquake losses in port systems." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43615.
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Ports play a critical role in transportation infrastructure, but are vulnerable to seismic hazards. Downtime and reduced throughput from seismic damage in ports results in significant business interruption losses for port stakeholders. Current risk management practices only focus on the effect of seismic hazards on individual port structures. However, damage and downtime of these structures has a significant impact on the overall port system's ship handling operations and the regional, national, and even international economic impacts that result from extended earthquake-induced disruption of a major container port. Managing risks from system-wide disruptions resulting from earthquake damage has been studied as a central element of a Grand Challenge project sponsored by the National Science Foundation Network for Earthquake Engineering Simulation (NEES) program. The following thesis presents the concepts and methods developed for the seismic risk management of a port-wide system of berths. In particular the thesis discusses the framework used to calculated port losses: the use of spatially correlated ground motion intensity measures to estimate damage to pile-supported marginal wharves and container cranes of various configurations via fragility relationships developed by project team members, repair costs and downtimes subsequently determined via repair models for both types of structures, and the impact on cargo handling operations calculated via logistical models of the port system. Results are expressed in the form of loss exceedance curves than include both repair/replacement costs and business interruption losses. The thesis also discusses how the results from such an analysis might be used by port decision makers to make more informed decisions in design, retrofit, operational, and other seismic risk management options.
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Каткова, Тетяна Ігорівна. "Моделі і методи оцінки, прогнозування та управління стратегічною діяльністю підприємства в умовах невизначеності." Thesis, НТУ "ХПІ", 2018. http://repository.kpi.kharkov.ua/handle/KhPI-Press/35128.
Full textAbstract:
Дисертація на здобуття наукового ступеня доктора технічних наук за спеціальністю 05.13.03 – системи та процеси керування – Національний технічний університет "Харківський політехнічний інститут", Харків 2018.
Дисертаційну роботу присвячено вирішенню важливої та актуальної проблеми наукового обґрунтування і розробки комплексу моделей і методів оцінки, прогнозування та управління стратегічною діяльністю підприємства в умовах невизначеності. Розроблено моделі та методи оцінки і прогнозування стану об'єктів в умовах невизначеності з великим числом можливих станів і великим числом нечітко заданих факторів. Сформульовано і реалізовано концепцію системного стратегічного фінансового планування, що забезпечує комплексне рішення приватних задач стратегічного фінансового планування та управління станом підприємства з урахуванням їх взаємозалежності і взаємозв'язку. Запропоновано економіко-математичні моделі вибору стратегічних напрямків діяльності підприємства, що дозволило врахувати відмінності в рентабельності, рівнях ризику, розмірах розміщеного капіталу. Розроблено моделі та методи управління розподілом активів підприємства по стратегічних напрямках діяльності для кожної зі стадій багатокрокового управління інвестиційним портфелем підприємства з урахуванням відмінностей їх рентабельності та рівня ризику. Обґрунтовано комплекс математичних моделей і методів системного вирішення сукупності оптимізаційних задач вибору проекту плану матеріально-технічного розвитку з урахуванням обсягу вкладених коштів, рівня позикових коштів і леверидж-ефекту, що виникає при цьому. Розроблено моделі та методи розв'язання задач управління інвестиційним портфелем, що враховують невизначеність і ризик при оцінюванні стану зовнішнього середовища, а також рівня можливого прибутку від діяльності підприємства. Розглянуто та удосконалено моделі динаміки вартості активів в умовах ризику і невизначеності. Запропоновано математичну модель марківської динаміки вартості у марківському зовнішньому середовищі.Thesis for the degree of Doctor of Engineering in specialty 05.13.03 – systems and management processes. – National Technical University "Kharkov Polytechnic Institute", Kharkov, 2018. The thesis is devoted to the solution of an important and actual problem of the scientific substantiation and development of a complex of models and methods for assessing, forecasting and managing the strategic activity of an enterprise under uncertainty. Models and methods for estimating and predicting the state of objects under conditions of uncertainty with a large number of possible states and a large number of fuzzy factors are developed. The concept of strategic financial planning has been formulated and implemented, providing a comprehensive solution of particular problems of strategic financial planning and management of the enterprise condition taking into account their interdependence and interconnection. Economic and mathematical models for choosing strategic directions of the enterprise's activities were proposed, which allowed taking into account differences in profitability, risk levels, and the size of the allocated capital. The models and methods of managing the distribution of the company's assets by strategic lines of activity for each of the stages of multi-step management of the enterprise's investment portfolio, taking into account the differences in their profitability and the level of risk are developed. The complex of mathematical models and methods of the system solution of a set of optimization tasks for the selection of the draft plan for material and technical development is substantiated, taking into account the amount of funds invested, the level of borrowed funds and the resulting leverage effect. Models and methods for solving investment portfolio management problems have been developed, taking into account uncertainty and risk in assessing the state of the external environment, as well as the level of possible profit from the activities of the enterprise. Models of the dynamics of the value of assets under risk and uncertainty are reviewed and improved. A mathematical model of the Markovian value dynamics in Markov's environment is proposed.
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Marshall, Richard Carel. "A state space forecasting approach to commodity futures trading." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185667.
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State space forecasting originated in the mid-1970's from engineering models based upon the Kalman filter. To date, the application of state space forecasting to commodity and financial markets has been limited. This study examines a system for trading futures contracts using state space forecasts of commodity futures prices. The system is evaluated for a speculative asset (gold) and a nonspeculative commodity (copper). Price forecasts are developed from multivariate state space models, and the variables considered in the models are those which, according to economic theory, may influence price movements. It is demonstrated that the relative importance of the different economic variables changes over time. Through simulated trading of copper and gold futures contracts, it is also shown that significant profits can be generated from the state space forecasting approach, especially when prices are trending upward or downward fairly continuously. Relaxing the position limit of one contract substantially increases profits. The results suggest that the copper and gold futures markets may be inefficient in the semistrong sense.
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Bae, Kyungcho. "Energy consumption forecasting: Econometric model vs state space model." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/187010.
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This study examines the forecasting performance of two major multivariate methodologies: econometric modeling and multivariate state space modeling. The same variables are used in both models to facilitate comparison. They are evaluated by both expost and exante accuracy of U.S. energy consumption forecasts. Econometric models are highly simplified and a model selection procedure is applied to the models. Two different formats of multivariate state space models are examined: economic structure and identity structure. Goodrich's algorithm is employed to estimate the state space models. The state space models in both the econometric structure and the identity structure provided generally good estimates, usually, but not always, these forecasts were more accurate than those by the single econometric models.
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AMARAL, MARCELO RUBENS DOS SANTOS DO. "STATE SPACE MODELS: MULTIVARIATE FORMULATION APPLIED TO LOAD FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1996. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8707@1.
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CENTRO DE PESQUISA DE ENERGIA ELÉTRICAOs métodos de análise de séries temporais têm se revelado uma importante ferramenta de apoio à tomada de decisões, com importância crescente em um mundo cada vez mais globalizado. Esse fato pode ser ilustrado, entre muitos outros, através de um convênio firmado entre o CEPEL, o Núcleo de Estatística Computacional da PUC/RJ e a Eletrobrás, para se avaliar a utilidade dessas ferramentas nas etapas do planejamento do setor elétrico brasileiro. A metodologia em Espaço de Estado proporcionou o surgimento de duas importantes classes de modelos de previsão e análise de séries temporais completamente alternativas (os modelos estruturais e os modelos de inovações em espaço de estado), e, por isso, podem por vezes, causar dúvidas quando se fala em métodos de previsão em espaço de estado sem se especificar sobre qual das duas se está falando. Foi escolhido uma técnica específica e facilmente executável em softwares comerciais para cada classe de modelos: O desenvolvimento clássico de Harvey implementado no software STAMP, representando os modelos estruturais; e o desenvolvimento de Goodrich implementado no software FMP, representando os modelos de inovações. Essas técnicas estão tratadas de uma forma aprofundada, para proporcionar um melhor entendimento teórico das diferenças existentes entre ambas. Com o intuito de se avaliar a performance frente às outras técnicas existentes, são comparados os resultados das previsões entre as metodologias a partir de um sistema de comparação baseado nas estatísticas MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Squared Error) e U-Theil. Para tanto são vistos sucintamente as técnicas: Alisamento Exponencial (Holt-Winters), Box & Jenkins e Redes Neurais. Todas as técnicas foram aplicadas aos dados de consumo de energia elétrica das 32 empresas concessionárias do setor no Brasil, além de comparadas com as previsões realizadas por essas concessionárias. A novidade deste trabalho para o projeto em andamento está na aplicação multivariada possível através da metodologia de Goodrich.
The analysis of time series is, nowadays one of the most important tools in the decision making process, due mainly to the globalization of the world. As an illustration of that we can mention the recent contract signed between NEC/PUC-Rio and CEPEL/Eletrobrás, where time series techniques are to be used in the planning process of the brazilian sector. The state-space approach forms the basis of two important forecasting models to time series analysis the structural model and the state space innovation model. Because of that one finds it difficult to have a clear cut definition of either one of them. These two models formulation were implemented in comercial softwares: the structural model of A. Harvey in STAMP and the state space innovation of R. Goodrich in FMP. In order to check the perfomance of these state space approaches vis-à-vis the traditional forecasting techniques, it was used the following statistics: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Squared Error) and U-Theil. The traditional approaches used in the comparison were: Holt-Winters, Box & Jenkins and Backpropagation Neural Network. All the methods, included the state space ones were applied to the demand series of 32 electrical utilities which form the brazilian electrical distribution system. If was also attempted the multivariate state-space formulation of R. Goodrich which is included in FMP software.
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Hodges, Duncan David. "Propagation forecasting for EHF and SHF systems." Thesis, University of Bath, 2006. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436876.
Full text
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Muthiah, Sathappan. "Design and Maintenance of Event Forecasting Systems." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102866.
Full textAbstract:
With significant growth in modern forms of communication such as social media and micro-
blogs we are able to gain a real-time understanding into events happening in many parts of
the world. In addition, these modern forms of communication have helped shed light into the
increasing instabilities across the world via the design of anticipatory intelligence systems [45,
43, 20] that can forecast population level events like civil unrest, disease occurrences with
reasonable accuracy. Event forecasting systems are generally prone to become outdated
(model drift) as they fail to keep-up with constantly changing patterns and thus require
regular re-training in order to sustain their accuracy and reliability. In this dissertation we
try to address some of the issues associated with design and maintenance of event forecasting
systems in general. We propose and showcase performance results for a drift adaptation
technique in event forecasting systems and also build a hybrid system for event coding which
is cognizant of and seeks human intervention in uncertain prediction contexts to maintain a
good balance between prediction-fidelity and cost of human effort. Specifically we identify
several micro-tasks for event coding and build separate pipelines for each with uncertainty
estimation capabilities and thereby be able to seek human feedback whenever required for
each micro-task independent of the rest.Doctor of Philosophy
Event forecasting systems help reduce violence, loss/damage to humans and property. They find applicability in supply chain management, prioritizing citizen grievances, designing mea- sures to control violence and minimize disruptions and also in applications like health/tourism by providing timely travel alerts. Several issues exist with the design and maintenance of such event forecasting systems in general. Predictions from such systems may drift away from ground reality over time if not adapted to various shifts (or changes) in event occurrence patterns in real-time. A continuous source of ground-truth events is of paramount necessity for the continuous maintenance of forecasting systems. However ground-truth events used for training may not be reliable but often information about their uncertainty is not reflected in the systems that are used to build the ground truth. This dissertation focuses on addressing such issues pertaining to design and maintenance of event forecasting systems. We propose a framework for online drift-adaptation and also build machine learning methods capable of modeling and capturing uncertainty in event detection systems. Finally we propose and built a hybrid event coding system that can capture the best of both automated and manual event coders. We breakdown the overall event coding pipeline into several micro-tasks and propose individual methods for each micro-task. Each method is built with the capability to know what it doesn't know and thus is capable of balancing quality vs throughput based on available human resources.
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Piovani, Duccio. "Analysing and forecasting transitions in complex systems." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/31380.
Full textAbstract:
We analyse in detail a new approach to the monitoring and forecasting of the onset of transitions in high dimensional complex systems by application to the Tangled Nature model of evolutionary ecology and high dimensional replicator systems with a stochastic element, the Stochastic Replicator model. A high dimensional stability matrix is derived for the mean field approximation to the stochastic dynamics. This allows us to determine the stability spectrum about the observed quasi-stable configurations. From overlap of the instantaneous configuration vector of the full stochastic system with the eigenvectors of the unstable directions of the deterministic mean field approximation we are able to construct a good early-warning indicator of the transitions occurring intermittently.
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Loo, Siew Lan. "Neural networks for financial forecasting." Thesis, University College London (University of London), 1994. http://discovery.ucl.ac.uk/1317942/.
Full textAbstract:
Neural networks demonstrate great potential for discovering non-linear relationships in time-series and extrapolating from them. Results of forecasting using financial data are particularly good [LapFar87, Schöne90, ChaMeh92]. In contrast, traditional statistical methods are restrictive as they try to express these non-linear relationships as linear models. This thesis investigates the use of the Backpropagation neural model for time-series forecasting. In general, neural forecasting research [Hinton87] can be approached in three ways: research into, the weight space, into the physical representation of inputs, and into the learning algorithms. A new method to enhance input representations to a neural network, referred to as model sNx, has been developed. It has been studied alongside a traditional method in model N. The two methods reduce the unprocessed network inputs to a value between 0 and 1. Unlike the method in model N, the variants of model sNx, sN1 and sN2, accentuate the contracted input value by different magnitudes. This different approach to data reduction exploits the characteristics of neural extrapolation to achieve better forecasts. The feasibility of the principle of model sNx has been shown in forecasting the direction of the FFSE-100 Index. The experimental strategy involved optimisation procedures using one data set and the application of the optimal network from each model to make forecasts on different data sets with similar and dissimilar patterns to the first. A Neural Forecasting System (NFS) has been developed as a vehicle for the research. The NFS offers historical and live simulations, and supports: a data alignment facility for standardising data files with non-uniform sampling times and volumes, and merging them into a spreadsheet; a parameter specification table for specifications of neural and system control parameter values; a pattern specification language for specification of input pattern formation using one or more time-series, and loading to a configured network; a snapshot facility for re-construction of a partially trained network to continue or extend a training session, or re-construction of a trained network to forecast for live tests; and a log facility for recording experimental results. Using the NFS, specific pattern features selected from major market trends have been investigated [Pring8O]: triple-top ('three peaks'), double-top ('two peaks'), narrow band ('modulating'), bull ('rising') and recovery ('U-turn'). Initially, the triple-top pattern was used in the N model to select between the logarithmic or linear data form for presenting raw input data. The selected linear method was then used in models sN1, sN2 and N for network optimisations. Experiments undertaken used networks of permutations of sizes of input nodes (I), hidden nodes (H), and tolerance value. Selections were made for: the best method, by value, direction, or value and direction, for measuring prediction accuracy; the best configuration function, H - I 4), with 4) equal to 0.9, 2 or 3; and the better of sN1 and sN2. The evaluation parameters were, among others, the prediction accuracy (%), the weighted return (%), the Relative Threshold Prediction Index (RTPI) indicator, the forecast error margins. The RTPI was developed to filter out networks forecasting above a minimum prediction accuracy with a credit in the weighted return (%). Two optimal networks, one representing model sNx and one N were selected and then tested on the double-top, narrow band, bull and recovery patterns. This thesis made the following research conthbutions. • A new method in model sNx capable of more consistent and accurate predictions. • The new RTPI neural forecasting indicator. • A method to forecast during the consolidation ('non-diversifying') trend which most traditional methods are not good at. • A set of improvements for more effective neural forecasting systems.
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Baldwin, Alexander (Alexander Lee), and Jaesung Shin. "New product forecasting in volatile markets." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/92639.
Full textAbstract:
Thesis: M. Eng. in Logistics, Massachusetts Institute of Technology, Engineering Systems Division, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 47-48).
Forecasting demand for limited-life cycle products is essentially projecting an arc trend of demand growth and decline over a relatively short time horizon. When planning for a new limited-life product, many marketing and production decisions depend on accurately predicting the life cycle effects on product demand. For products with stable market shares, forecasting demand over the life cycle benefits from the high degree of correlation with prior sales of similar products. But for volatile-share markets, rapid innovation continually alters the shape of available features and performance, leading to products with demand patterns that differ greatly from prior generations and forecasting techniques that rely more on judgment and naive expectations. In an effort to understand opportunities and limitations of quantitative forecasting in a specific volatile-market context, we hypothesized certain characteristics about the shape and volatility of the demand trend in volatile-market product, and tested them using sample stable and volatile market data from a partner firm. We found significant differences in quantifiable characteristics such as skew and variance over the life cycle, presenting an opportunity for supply chain stakeholders to incorporate life cycle effects into forecasting models.
by Alexander Baldwin and Jaesung Shin.
M. Eng. in Logistics
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Trepte, Kai, and Rajaram Narayanaswamy. "Forecasting consumer products using prediction markets." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53546.
Full textAbstract:
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2009.Includes bibliographical references (leaves 105-106).
Prediction Markets hold the promise of improving the forecasting process. Research has shown that Prediction Markets can develop more accurate forecasts than polls or experts. Our research concentrated on analyzing Prediction Markets for business decision-making. We configured a Prediction Market to gather primary data, sent out surveys to gauge participant views and conducted in-depth interviews to explain trader behavior. Our research was conducted with 169 employees from General Mills who participated in Prediction Markets that lasted from two to ten weeks. Our research indicates that short term forecasting Prediction Markets are no more accurate than conventional forecasting methods. It also presents and addresses three interesting contradictions. First, the Sales Organization won the majority of the Prediction Markets, yet the overall performance of Sales as a group was worse than that of other groups. Second, Prediction Markets were able to gain access to more information than General Mills' current process, yet the impact on forecast accuracy was not significant. Third, with a MAPE of 11% for promotional Prediction Markets, it would seem that promotional demand was well understood up-front, yet when we dissected the promotional forecasts we discovered that participants changed their minds over time degrading overall forecast accuracy. We believe that we have extended the current body of work on Prediction Markets in ways that will increase the utilization in business environments.
by Kai Trepte and Rajaram Narayanaswamy.
M.Eng.in Logistics
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naz, saima. "Forecasting daily maximum temperature of Umeå." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-112404.
Full textAbstract:
The aim of this study is to get some approach which can help in improving the predictions of daily temperature of Umeå. Weather forecasts are available through various sources nowadays. There are various software and methods available for time series forecasting. Our aim is to investigate the daily maximum temperatures of Umeå, and compare the performance of some methods in forecasting these temperatures. Here we analyse the data of daily maximum temperatures and find the predictions for some local period using methods of autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and cubic splines. The forecast package in R is used for this purpose and automatic forecasting methods available in the package are applied for modelling with ARIMA, ETS, and cubic splines. The thesis begins with some initial modelling on univariate time series of daily maximum temperatures. The data of daily maximum temperatures of Umeå from 2008 to 2013 are used to compare the methods using various lengths of training period. On the basis of accuracy measures we try to choose the best method. Keeping in mind the fact that there are various factors which can cause the variability in daily temperature, we try to improve the forecasts in the next part of thesis by using multivariate time series forecasting method on the time series of maximum temperatures together with some other variables. Vector auto regressive (VAR) model from the vars package in R is used to analyse the multivariate time series. Results: ARIMA is selected as the best method in comparison with ETS and cubic smoothing splines to forecast one-step-ahead daily maximum temperature of Umeå, with the training period of one year. It is observed that ARIMA also provides better forecasts of daily temperatures for the next two or three days. On the basis of this study, VAR (for multivariate time series) does not help to improve the forecasts significantly. The proposed ARIMA with one year training period is compatible with the forecasts of daily maximum temperature of Umeå obtained from Swedish Meteorological and Hydrological Institute (SMHI).
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Castorina, Giovanni. "Artificial intelligence based hybrid systems for financial forecasting." Thesis, University of the West of England, Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365146.
Full textAbstract:
Current research carried out on financial forecasting has highlighted some limitations of classical econometric methods based on the assumption that the investigated time series can be described as stationary stochastic processes with Gaussian probability density functions. Chaotic behaviour, fractal characteristics and non-linear dynamics have been emerging in different aspects of the financial forecasting problem. The objective of this thesis is to take a system level perspective of the financial forecasting problem and to explore a number of approaches to enhance more 'traditional' decision making flows for stock market forecasting, with particular emphasis on stock selection and timing. To achieve this purpose, a number of stock selection and timing computational 'modules' are investigated. From a computational point of view, the investigation performed in this work encompass techniques such as artificial neural networks, genetic algorithms, chaos theory and fractal geometry, as well as more traditional methods such as clustering, screening, ranking, and statistics based models. From a financial data point of view, this research takes advantage of both fundamental and technical information to enhance the stock selection and timing processes and to cover several investment horizons. Three computational modules are proposed. First, a multivariate stock ranking module which uses fundamental information and is optimised through genetic algorithms. Second, a multivariate forecasting module which uses technical information and is based on artificial neural networks. Third, a univariate price time series forecasting module based on artificial neural networks. In addition, an integrated flow that takes advantage of some synergies and complementary properties of the devised modules is proposed. The effectiveness of the developed modules and the viability of the proposed integrated flow are evaluated over a number of investment horizons using (out-of-sample) historical data.
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Feng, Haitang. "Data management in forecasting systems : optimization and maintenance." Phd thesis, Université Claude Bernard - Lyon I, 2012. http://tel.archives-ouvertes.fr/tel-00997235.
Full textAbstract:
Forecasting systems are usually based on data warehouses for data strorage, and OLAP tools for historical and predictive data visualization. Aggregated predictive data could be modified. Hence, the research issue can be described as the propagation of an aggregate-based modification in hirarchies and dimensions in a data warehouse enironment. Ther exists a great number of research works on related view maintenance problems. However, to our knowledge, the impact of interactive aggregate modifications on raw data was not investigated. This CIFRE thesis is supported by ANRT and the company Anticipeo. The application of Anticipeo is a sales forecasting system that predicts future sales in order to draw appropriate business strategy in advance. By the beginning of the thesis, the customers of Anticipeo were satisfied the precision of the prediction results, but not with the response time. The work of this thesis can be generalized into two parts. The first part consists in au audit on the existing application. We proposed a methodology relying on different technical solutions. It concerns the propagation of an aggregate-based modification in a data warehouse. the second part of our work consists in the proposition of a newx allgorithms (PAM - Propagation of Aggregated-baseed Modification) with an extended version (PAM II) to efficiently propagate in aggregate-based modification. The algorithms identify and update the exact sets of source data anf other aggregated impacted by the aggregated modification. The optimized PAM II version archieves better performance compared to PAM when the use of additional semantics (e.g. dependencies) is possible. The experiments on real data of Anticipeo proved that the PAM algorithm and its extension bring better perfiormance when a backward propagation.
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Hirschman, Edward. "Comprehensive forecasting of software integrity in C3I systems." Master's thesis, Virginia Tech, 1992. http://hdl.handle.net/10919/42019.
Full textAbstract:
The purpose of this project is to forecast the incidence
of failures to be encountered by a software package for C3I
systems over and throughout its life cycle.
It will be assumed that a data base of software
previously developed for C3I systems will be used to forecast
the software integrity of a software package under initial
development. "Software integrity" is defined as a projection
of the stream of failures that will be experienced by the new
software. The failure history of the mature C3I systems
software will be statistically quantified parametrically and
by experimental design techniques (ANOVA) to gather
information which will be used to forecast what C3I software
with similar characteristics--length, language, debugging
effort, etc.--will experience.
Then, as the new C3I system software matures, statistical techniques for software systems engineering will be addressed for testing appropriateness of the initial projections; and eventually the new software will be parametrically modeled on its own merits to forecast the failures to be encountered over the remainder of its life cycle.
Lastly, the data base history of software for mature C3I systems software will be updated and amended as needed to facilitate reliable forecasting of software integrity for a new round of C3I systems software.
The attention to C3I implied by the title of the project
will reflect itself in the classes of software considered and
development conditions, schedules and complexities of the
software.
Master of Science
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Niu, Mu. "Supply chain dynamics and forecasting." Thesis, Northumbria University, 2009. http://nrl.northumbria.ac.uk/1606/.
Full textAbstract:
Nowadays, the global supply chain system needs to respond promptly to changes in customer demand and adapt quickly to advancements in technology. Supply chain management becomes an integral approach which links together producers, distributors and customers in collaborative management of the whole system. The variability in orders or inventories in supply chain systems is generally thought to be caused by exogenous random factors such as uncertainties in customer demand or lead time. Studies have shown, however, that orders or inventories may exhibit significant variability, even if customer demand and lead time are deterministic. Most researchers have concentrated on the effects of the ordering policy on supply chain behaviour, while not many have paid attention to the influences of applying different forecasting to supply chain planning. This thesis presents an analysis of the behaviour of a model of a centralised supply chain. The research was conducted within the manufacturing sector and involved the breathing equipment manufacturer Draeger Safety, UK. The modelling process was embedded in the organization and was focused on the client's needs. A simplified model of the Draeger Safety, UK centralised supply chain was developed and validated. The dynamics of the supply chain under the influence of various factors: demand pattern, ordering policy, demand-information sharing, and lead time were observed. Simulation and analysis were performed using system dynamics, non-linear dynamics and control theory. The findings suggest that destructive oscillations of inventory could be generated by internal decision making practices. To reduce the variation in the supply chain system, the adjustment parameters for both inventory and supply line discrepancies should be more comparable in magnitude. Counter- intuitively, in certain fields of decision, sharing demand information can do more harm than good. The linear forecasting ARMA (autoregression and moving average) model and the nonlinear forecasting model Wavelet Neural Network were applied as the supply chain forecasting methods. The performance was tested against supply chain costs. A management microworld was developed, allowing managers to experiment with different decision policies and learn how the supply chain performs.
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Chen, Yu. "FORECASTING WITH MIXED FREQUENCY DATA:MIDAS VERSUS STATE SPACE DYNAMIC FACTOR MODEL : AN APPLICATION TO FORECASTING SWEDISH GDP GROWTH." Thesis, Örebro universitet, Handelshögskolan vid Örebro Universitet, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-29475.
Full textAbstract:
Most macroeconomic activity series such as Swedish GDP growth are collected quarterly while an important proportion of time series are recorded at a higher frequency. Thus, policy and business decision makers are often confront with the problems of forecasting and assessing current business and economy state via incomplete statistical data due to publication lags. In this paper, we survey a few general methods and examine different models for mixed frequency issues. We mainly compare mixed data sampling regression (MIDAS) and state space dynamic factor model (SS-DFM) by the comparison experiments forecasting Swedish GDP growth with various economic indicators. We find that single-indicator MIDAS is a wise choice when the explanatory variable is coincident with the target series; that an AR term enables MIDAS more promising since it considers autoregressive behaviour of the target series and makes the dynamic construction more flexible; that SS-DFM and M-MIDAS are the most outstanding models and M-MIDAS dominates undoubtedly at short horizons up to 6 months, whereas SS-DFM is more reliable at long predictive horizons. And finally we conclude that there is no perfect winner because each model can dominate in a special situation.
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Hassanzadeh, Mohammadtaghi. "A New State Transition Model for Forecasting-Aided State Estimation for the Grid of the Future." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64407.
Full textAbstract:
The grid of the future will be more decentralized due to the significant increase in distributed generation, and microgrids. In addition, due to the proliferation of large-scale intermittent wind power, the randomness in power system state will increase to unprecedented levels. This dissertation proposes a new state transition model for power system forecasting-aided state estimation, which aims at capturing the increasing stochastic nature in the states of the grid of the future. The proposed state forecasting model is based on time-series modeling of filtered system states and it takes spatial correlation among the states into account. Once the states with high spatial correlation are identified, the time-series models are developed to capture the dependency of voltages and angles in time and among each other. The temporal correlation in power system states (i.e. voltage angles and magnitudes) is modeled by using autoregression, while the spatial correlation among the system states (i.e. voltage angles) is modeled using vector autoregression. Simulation results show significant improvement in power system state forecasting accuracy especially in presence of distributed generation and microgrids.Ph. D.
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Holbrook, Blair Sato. "Point-of-sale demand forecasting." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104397.
Full textAbstract:
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2016. In conjunction with the Leaders for Global Operations Program at MIT.Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2016. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 38).
Nike Always Available (AA) is a significant global business unit within Nike that allows retail customers to purchase athletic essentials at weekly replenishment intervals and 95% availability. However, demand fluctuations and current forecasting processes have resulted in frequent stock-outs and inventory surpluses, which in turn affect revenue, profitability, and brand trust. Potential root causes for demand fluctuations have included: -- Erratic customer behavior, including unplanned promotional events, allocation of open-to- buy dollars for futures (i.e., contract) versus replenishment (i.e., AA), and product inventory loading to protect from anticipated stock-outs; -- Lack of incentives and accountability to encourage accurate forecasting by customers. Current forecasting processes, which utilize historical sell-in data (i.e., product sold to retail customers) were found to be significantly inaccurate - 100% MAPE. The goal of this project was to develop a more accurate forecast based on historical sell-through data (i.e., product sold to consumers), which were recently made available. Forecast error was drastically reduced using the new forecasting method - 35% MAPE. A pilot was initiated with a major retail customer in order to test the new forecast model and determine the effects of a more transparent ordering partnership. The pilot is ongoing at the time of thesis completion.
by Blair Sato Holbrook.
M.B.A.
S.M. in Engineering Systems
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Koottatep, Pakawkul, and Jinqian Li. "Promotional forecasting in the grocery retail business." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36142.
Full textAbstract:
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2006.Includes bibliographical references (leaves 84-85).
Predicting customer demand in the highly competitive grocery retail business has become extremely difficult, especially for promotional items. The difficulty in promotional forecasting has resulted from numerous internal and external factors that affect the demand patterns. It has also resulted from multiple levels of hierarchy that involve different groups in the organization as well as different methods and systems. Moreover, judgments from the forecasters are critical to the accuracy of the forecasts, while the value of tweaking the forecast results is yet to be determined. In this business, the forecasters generally have a high incentive to over-forecast in order to meet the corporate goal of maximizing customer satisfaction. The main objective of this thesis is to analyze the effectiveness of promotional forecasting, identify the factors contributing to forecast accuracy, and propose suggestions for improving forecasts. In light of this objective, we used WMPE and WMAPE as the measures of forecast accuracy, and conducted analysis of promotional forecast accuracy from different point of views.
(cont.) We also verified our results with regression analysis, which helped identify the significance of each forecasting attribute so as to support the promotion planning without compromising forecast accuracy. We suggest several approaches to improve forecast accuracy. First, to improve store forecasts, we recommend three models: the bias correction model, the adaptive bias correction model, and the regression model. Second, to improve replenishment forecasts, we propose a new model that combines the top-down and bottom-up approaches. Lastly, we suggest a framework for measuring accuracy that emphasizes the importance of comparing the accuracy of forecasts generated from systems and from judgments.
by Pakawkul Koottatep and Jinqian Li.
M.Eng.in Logistics
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Scott, Paul J. "Minimal dimension state space identification, theory and applications in climate forecasting." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ57193.pdf.
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Unosson, Måns. "A Mixed Frequency Steady-State Bayesian Vector Autoregression: Forecasting the Macroeconomy." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297406.
Full textAbstract:
This thesis suggests a Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is proposed to sample the posterior distribution. A forecast study is performed using monthly and quarterly data for the US macroeconomy between 1964 and 2008. The proposed model is compared to a steady-state Bayesian VAR model estimated on data aggregated to quarterly frequency and a quarterly least squares VAR with standard parametrization. Forecasts are evaluated using root mean squared errors and the log-determinant of the forecast error covariance matrix. The results indicate that the inclusion of monthly data improves the accuracy of quarterly forecasts of monthly variables for horizons up to a year. For quarterly variables the one and two quarter forecasts are improved when using monthly data.
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Poutiainen, Zacharias. "Short-Term Heat Load Forecasting in District Heating Systems : A Comparative Study of Various Forecasting Methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265670.
Full textAbstract:
Short term heat load forecasts are vital for optimal production planning and commitment of generation units. The generation utility also bares a balance responsibility toward the electricity market as a result of CHP generation. Sub-optimal load forecasts can lead to high costs relating to unit commitment, fuel usage and balancing costs. This thesis presents the empirical comparison of various models for 24h heat load forecasting. Five methods were investigated including four supervised machine learning algorithms; neural networks, support vector machines, random forests and boosted decision trees and one auto-regressive time series model; ARIMAX. The models were developed, and evaluated using cross validation with one year of hourly heat load data from a local district heating system and corresponding meteorological data from the same time period. The thesis also investigates the impact of feature selection on the predictive power and generalization ability of the models. The results indicate a significant difference in forecast accuracy between the methods with neural networks and ARIMAX showing the best and similar performance followed by the support vector machine, boosted decision trees and random forest.Korttidsprognoser för fjärrvärmelast är mycket viktiga för optimal produktionsplanering. Energibolag som använder kraftvärme bär dessutom balansansvar gentemot elmarknaden. Sub-optimala lastprognoser kan leda till höga kostnader för start och stopp, bränsleåtgång och obalanser. Detta examensarbete presenterar den empiriska jämförelsen av olika modeller avseende 24-timmars lastprognostisering. Totalt fem metoder undersöktes varav fyra maskininlärningsalgoritmer; neurala nätverk, stödvektormaskin, random forest samt boosted desicion trees och en tidsseriemodell; ARIMAX. Modellerna utvecklades, och utvärderades med hjälp av korsvalidering på ett års värden av timvis lastdata från ett lokalt fjärrvärmenät och motsvarande väderdata för samma tidsperiod. Examensarbetet undersöker även inverkan av variabelselektion på prognosernas precision och förmåga att generalisera. Resultaten tyder på en signifikant skillnad i noggrannhet mellan de olika modellerna. Bäst resultat uppnåddes av neurala nätverk och ARIMAX med en liten skillnad sinsemellan, följt av stödvektormaskin, boosted decision trees och random forest.
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Asimakopoulos, Stavros. "A human-computer interaction perspective on forecasting systems design." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527179.
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Velazquez, Zapata Juan Alberto. "Evaluation of hydrological ensemble prediction systems for operational forecasting." Thesis, Université Laval, 2010. http://www.theses.ulaval.ca/2010/27792/27792.pdf.
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Hirschman, Edward. "Comprehensive forecasting of software integrity in C I systems /." This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-04122010-083444/.
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ZEBULUM, RICARDO SALEM. "NEURAL NETWORKS IN LOAD FORECASTING IN ELECTRIC ENERGY SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1995. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9514@1.
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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOREsta dissertação investiga a utilização de Redes Neurais Artificiais (RNAs) na área de previsão de carga elétrica. Nesta investigação foram utilizados dados reais de energia relativos ao sistema elétrico brasileiro. O trabalho consiste de quatro partes principais: um estudo sobre o problema de previsão de carga no contexto de sistemas elétricos de potência; o estudo e a modelagem das RNAs para previsão de carga; o desenvolvimento do ambiente de simulação; e o estudo de casos. O estudo sobre o problema de previsão de carga envolveu uma investigação sobre a importância da previsão de demanda de energia na área de sistemas elétricos de potência. Enfatizou-se a classificação dos diversos tipos de previsão de acordo com o seu horizonte, curto e longo prazo, bem como a análise das variáveis mais relevantes para a modelagem da carga elétrica. O estudo também consistiu da análise de vários projetos na área de previsão de carga, apresentando as metodologias mais utilizadas. O estudo e a modelagem de RNAs na previsão de carga envolveu um extenso estudo bibliográfico de diversas metodologias. Foram estudadas as arquiteturas e os algoritmos de aprendizado mais empregados. Constatou-se uma predominância da utilização do algoritmo de retropropagação (Backpropagation) nas aplicações de previsão de carga elétrica horária para curto prazo. A partir desse estudo, e utilizando o algoritmo de retropropagação, foram propostas diversas arquiteturas de RNAs de acordo com o tipo de previsão desejada. O desenvolvimento do ambiente de simulação foi implementado em linguagem C em estações de trabalho SUN. O pacote computacional engloba basicamente 3 módulos: um módulo de pré-processamento da série de carga para preparar os dados de entrada; um módulo de treinamento da Rede Neural para o aprendizado do comportamento da série; e um módulo de execução da Rede Neural para a previsão dos valores futuros da série. A construção de uma interface amigável para a execução do sistema de previsão, bem como a obtenção de um sistema portátil foram as metas principais para o desenvolvimento do simulador. O estudo de casos consistiu de um conjunto de implementações com o objetivo de testar o desempenho de um sistema de previsão baseado em Redes Neurais para dois horizontes distintos: previsão horária e previsão mensal. No primeiro caso, foram utilizados dados de energia da CEMIG (Estado de Minas Gerais) e LIGHT (Estado do Rio de Janeiro). No segundo caso, foram utilizados dados de energia de 32 companhias do setor elétrico brasileiro. Destaca-se que a previsão mensal faz parte de um projeto de interesse da ELETROBRÁS, contratado pelo CEPEL. Para ambos os casos, investigou-se a influência do horizonte de previsão e da época do ano no desempenho do sistema de previsão. Além disso, foram estudadas as variações do desempenho das Redes Neurais de acordo com a empresa de energia elétrica utilizada. A avaliação do desempenho foi feita através da análise das seguintes estatísticas de erro: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Square Error) e U de Theil. O desempenho das RNAs foi comparado com o de outras técnicas de previsão, como os métodos de Holt-Winters e Box & Jenkins, obtendo-se resultados, em muitos casos, superiores.
This dissertation investigates the application of Artificial Neural Networks (ANNs) in load forecasting. In this work we have used real load data from the Brazilian electrical system. The dissertation is divided in four main topics: a study of the importance of load forecasting to electric power systems; the investigation of the ANN modeling to this particular problem; the development of a neuro-simulador; and the case studies. It has been made an investigation of the objectives of load forecasting to power systems. The different kinds of load forecasting have been classified according to the leading time of the prediction (short and long term). The more important variables to model electric load were also investigated. This study analyses many projects in the area of load forecasting and presents the techniques that have been traditionally used to treat the problem. The ANNs modeling to load forecasting involved a deep investigation of works that have been published. The ANNs architectures and learning algorithms more commonly used were studied. It has been verified that the Backpropagation algorithm was the more commoly applied in the problem (particularly, in the problem of short term hourly load forecasting). Based on this investigation and using the backpropagation algorithm, many Neural Networks architetures were proposed according to the desired type of forecasting. The development of the neuro-simulator has been made in C language, using SUN workstations. The software is divided in 3 modules: a load series pre-processing module, to prepare the input data; a training module to the load series behavior learning; and an execution module, in which the Neural Network will perform the predictions. The development of a friendly interface to the forecasting system execution and the portability of the system were main goals during the simulator development. The case studies involved testing the system performance for 2 cases: hourly and monthly predictions. In the first case, load data from CEMING (State of Minas Gerais) and LIGHT (State of Rio de Janeiro) has been used. In the second case load data from 32 companies of the Brazilian electrical system has been used. Monthly load forecasting is involved in a project of interest of two companies of the electric sector in Brazil: CEPEL and ELETROBRÁS. In both cases, influences of the forecasting horizon and of the period of the year in the system´s performance has been investigated. Besides, the changes in the forecasting performance according to the particular electric company were also studied. The performance evaluation has been done through the analysis of the following error figures: MAPE (Mean Absolute Percentage Error), RMSE (Root Mean Square Error) and Theil´s U. The ANN performance was also compared with the performance of other techniques, like Holt-Winteres and Box-Jenkins, giving better results in many cases.
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Velázquez, Zapata Juan Alberto. "Evaluation of hydrological ensemble prediction systems for operational forecasting." Doctoral thesis, Université Laval, 2010. http://hdl.handle.net/20.500.11794/22245.
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La prévision hydrologique consiste à évaluer quelle sera l'évolution du débit au cours des prochains pas de temps. En utilisant les systèmes actuels de prévisions hydrologiques déterministes, il est impossible d'apprécier simplement l'incertitude associée à ce type de prévision, ce que peut nuire à la prise de décisions. La prévision hydrologique d'ensemble (PHE) cherche à étayer cette incertitude en proposant, à chaque pas de temps, une distribution de probabilité, la prévision probabiliste, en place et lieu d'une estimation unique du débit, la prévision déterministe. La PHE offre de nombreux bénéfices : elle informe l'utilisateur de l'incertitude; elle permet aux autorités qui prennent des décisions de déterminer des critères d'alerte et de mettre en place des scénarios d'urgence; elle fournit les informations nécessaires à la prise de décisions tenant compte du risque. L'objectif principal de cette thèse est l'évaluation de prévisions hydrologiques d'ensemble, en mettant l'accent sur la performance et la fiabilité de celles-ci. Deux techniques pour construire des ensembles sont explorées: a) une première reposant sur des prévisions météorologiques d'ensemble (PME) et b) une seconde exploitant simultanément un ensemble de modèles hydrologiques (multimodèle). En termes généraux, les objectifs de la thèse ont été établis afin d'évaluer : a) les incertitudes associées à la structure du modèle : une étude qui repose sur des simulations journalières issues de dix-sept modèles hydrologiques globaux, pour plus de mille bassins versants français; b) les incertitudes associées à la prévision météorologique : une étude qui exploite la PME du Service Météorologique du Canada et un modèle hydrologique opérationnel semi-distribué, pour un horizon de 3 jours sur douze bassins versants québécois; c) les incertitudes associées à la fois à la structure du modèle et à la prévision météorologique : une étude qui repose à la fois sur la PME issue du ECMWF (European Centre for Medium-Range Weather Forecasts) et seize modèles hydrologiques globaux, pour un horizon de 9 jours sur 29 bassins versants français. Les résultats mets en évidence les avantages des systèmes probabilistes par rapport aux les déterministes. Les prévisions probabilistes sont toutefois souvent affectées par une sous dispersion de leur distribution prédictive. Elles exigent alors un post traitement avant d'être intégrées dans un processus de prise de décision. Plus intéressant encore, les résultats ont également montré le grand potentiel de combiner plusieurs sources d'incertitude, notamment celle associée à la prévision météorologique et celle associée à la structure des modèles hydrologiques. Il nous semble donc prioritaire de continuer à explorer davantage cette approche combinatoire.
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Li, Qinyun. "A systems dynamics perspective of forecasting in supply chains." Thesis, Cardiff University, 2014. http://orca.cf.ac.uk/69349/.
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Purpose: To evaluate the impact of forecasting on supply chain via a system dynamics perspective. Method/approach: Techniques from Control Theory (such as block diagram, z-transforms, Fourier transforms, Jury’s Inners approach, and frequency response analysis) and Time Series Analysis are used to investigate the performance of supply chains analytically. Simulation is also used to verify the results. Findings: This thesis provides a new and complete proof to the knowledge that Naïve, simple exponential smoothing, and Holt’s forecasting when used in the Order-Up-To (OUT) policy always produce the bullwhip effect for any demand pattern and for all lead-times. In terms of the bullwhip performance when Damped Trend (DT) forecasts are used in the OUT policy, the bullwhip effect is always generated for traditional parameter suggestions. However, the bullwhip avoidance behaviour occurs for some unconventional parameter values. Using these unconventional parameter values, the DT / OUT system acts like a low-pass filter that can eliminate the bullwhip effect and maintain good inventory performance at the same time. The thesis also proves that the Proportional Order-Up-To (POUT) policy is able to reduce system nervousness at the manufacturer. Moreover, the proportional future guidance (PFG) mechanism proposed may reduce system nervousness and inventory costs at the manufacturer and reduce the bullwhip effect in the supply chain simultaneously. Implications: This thesis shows that the bullwhip and net stock variance reduction behaviours exist when unconventional parameter values are used in the DT forecasting procedure. It is the first evidence that it is possible to design a system with good financial performance but without directly looking into the performance of forecasting. The thesis is also the first to consider the MRP nervousness problem and the bullwhip effect at the same time. The PFG method proposed is easy to understand, and since it does not require sophisticated integrated IT systems, or demand / inventory information sharing, it should be easy to implement.
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Arad, Ron 1973. "Sterilization resource forecasting in the medical devices industry." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33333.
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Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2005.Includes bibliographical references (leaf 73).
Sterilization is an example of a procedure that has been outsourced by medical device companies. Sterilization is required for all medical devices and the process used is based on product type. As demand for medical devices increases, production is ramping up, and the need for additional sterilization capacity increases. The time required to build more sterilization capacity can be between six to nine months, and therefore companies are looking into their future production to estimate when will be the right time to start building more capacity. This thesis analyzes the change in sterilization capacity utilization using a simulation model. The model replicates the current production distribution based on data provided from the sterilization facility.
y Ron Arad.
M.Eng.in Logistics
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Sayyaddelshad, Saleh. "State estimation of nonlinear systems." Licentiate thesis, Luleå tekniska universitet, Signaler och system, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-25774.
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Observer design for nonlinear systems is a popular problem in control theory that has beenstudied from many angles. Since the system state variables, in general, are not available, stateestimation is essential in many control applications, which is why the problem is so attractivefor researchers. One example of a process that has nonlinear dynamics is wood drying.In the wood drying process, there are some unmeasurable variables such as the moisturecontent at the surface and inside the wood, which are important for controlling the drying processfor the purpose of minimizing the energy consumption of the wood drying kiln. However,to the best of our knowledge, there is no straightforward observer design for the wood-dryingprocess in the current literature. In the first two research papers that compose this thesis, afterintroducing a state space realization of the wood drying process, a novel method for estimatingthe moisture content of the wood during drying is proposed.Compared to typical systems, observer design for nonlinear uncertain systems with timedelays, is significantly more complicated and thus attractive for research. In this thesis, theproblem of the observer design for a class of uncertain discrete-time nonlinear systems withunknown time delay has also been investigated. The study shows that by using upper and lowerbounds of the time delay, the time delay can be excluded in the observer structure. The thirdand fourth papers mostly focus on this topic based on an optimization approach.Godkänd; 2013; 20131026 (salsay); Tillkännagivande licentiatseminarium 2013-11-25 Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Saleh Sayyaddelshad Ämne: Reglerteknik/Automatic Control Uppsats: State Estimation of Nonlinear Systems Examinator: Professor Thomas Gustafsson, Institutionen för system- och rymdteknik, Luleå tekniska universitet Diskutant: Professor Alexander Medvedev, Avdelningen för systemteknik, Uppsala universitet Tid: Onsdag den 18 december 2013 kl 13.00 Plats: A1545, Luleå tekniska universitet
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Hansen, James A. "Adaptive observations in spatially-extended nonlinear dynamical systems." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284504.
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Pasquali, Flavia. "State space models for the analysis and forecasting of climatic time series." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23081/.
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We analyse climatic time series with state space models in order to compute the forecast distribution. The task is challenging since the temperature series are characterised by large temporal and cross-sectional dimensions. We modify and apply the three-step method proposed in Li et al. Journal of Econometrics 2020, which exploit the cross information in order to improve prediction.
We fit the linear Gaussian state space model to different univariate time series, estimating the model parameters with the Kalman filter and computing the prediction errors. The prediction error time series are then jointly analysed by means of a dynamic factor model. The estimation procedure follows the two-step approach suggested by Doz, Giannone, and Reichlin in the context of macro-economic time series nowcasting. Finally, the simulation smoother by Durbin and Koopman allows to sample scenarios conditional on the observed time series and to reconstruct the forecast distribution.
The results we obtained are promising. They demonstrate the feasibility of the entire procedure. Our explorations involved just a climatic parameter (the maximum temperature) and a reduced sample of data (8 years on a weekly basis for twenty climatic stations) , but we preliminarily tested the whole approach on much longer time series - up to 150 years - with a richer cross-sectional structure - up to 10.000 stations - experiencing viable computational times and very promising estimation results.
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Tang, Fan. "Structural time series clustering, modeling, and forecasting in the state-space framework." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/6002.
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This manuscript consists of two papers that formulate novel methodologies pertaining to time series analysis in the state-space framework.
In Chapter 1, we introduce an innovative time series forecasting procedure that relies on model-based clustering and model averaging. The clustering algorithm employs a state-space model comprised of three latent structures: a long-term trend component; a seasonal component, to capture recurring global patterns; and an anomaly component, to reflect local perturbations. A two-step clustering algorithm is applied to identify series that are both globally and locally correlated, based on the corresponding smoothed latent structures. For each series in a particular cluster, a set of forecasting models is fit, using covariate series from the same cluster. To fully utilize the cluster information and to improve forecasting for a series of interest, multi-model averaging is employed. We illustrate the proposed technique in an application that involves a collection of monthly disease incidence series.
In Chapter 2, to effectively characterize a count time series that arises from a zero-inflated binomial (ZIB) distribution, we propose two classes of statistical models: a class of observation-driven ZIB (ODZIB) models, and a class of parameter-driven ZIB (PDZIB) models. The ODZIB model is formulated in the partial likelihood framework. Common iterative algorithms (Newton-Raphson, Fisher Scoring, and Expectation Maximization) can be used to obtain the maximum partial likelihood estimators (MPLEs). The PDZIB model is formulated in the state-space framework. For parameter estimation, we devise a Monte Carlo Expectation Maximization (MCEM) algorithm, using particle methods to approximate the intractable conditional expectations in the E-step of the algorithm. We investigate the efficacy of the proposed methodology in a simulation study, and illustrate its utility in a practical application pertaining to disease coding.
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Jessen, Andreas, and Carina Kellner. "Forecasting Management." Thesis, University of Kalmar, Baltic Business School, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hik:diva-1868.
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In a world that is moving faster and faster, a company’s ability to align to market changes is becoming a major competitive factor. Forecasting enables companies to predict what lies ahead, e.g. trend shifts or market turns, and makes it possible to plan for it. But looking into the future is never an easy task.
“Prediction is very difficult, especially if it’s about the future.” (Niels Bohr, 1885-1962)
However, progress in the field of forecasting has shown that it is possible for companies to improve on forecasting practices. This master thesis looks at the sales forecasting practices in MNCs primarily operating in emerging and developing countries. We examine the whole process of sales forecasting, also known as forecasting management, in order to develop a comprehensive model for forecasting in this type of companies. The research is based on a single case study, which is then later generalized into broader conclusions.
The conclusion of this master thesis is that forecasting is a four-step exercise. The four stages we have identified are: Knowledge creation, knowledge transformation, knowledge use and feedback. In the course of these four stages a company’s sales forecast is developed, changed and used. By understanding how each stage works and what to focus on, companies will be able to improve their forecasting practices.
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Luo, Yi. "DECISION MAKING UNDER UNCERTAINTY IN DYNAMIC MULTI-STAGE ATTACKER-DEFENDER GAMES." Diss., The University of Arizona, 2011. http://hdl.handle.net/10150/204331.
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This dissertation presents efficient, on-line, convergent methods to find defense strategies against attacks in dynamic multi-stage attacker-defender games including adaptive learning. This effort culminated in four papers submitted to high quality journals and a book and they are partially published. The first paper presents a novel fictitious play approach to describe the interactions between the attackers and network administrator along a dynamic game. Multi-objective optimization methodology is used to predict the attacker's best actions at each decision node. The administrator also keeps track of the attacker's actions and updates his knowledge on the attacker's behavior and objectives after each detected attack, and uses this information to update the prediction of the attacker's future actions to find its best response strategies. The second paper proposes a Dynamic game tree based Fictitious Play (DFP) approach to describe the repeated interactive decision processes of the players. Each player considers all possibilities in future interactions with their uncertainties, which are based on learning the opponent's decision process (including risk attitude, objectives). Instead of searching the entire game tree, appropriate future time horizons are dynamically selected for both players. The administrator keeps tracking the opponent's actions, predicts the probabilities of future possible attacks, and then chooses its best moves. The third paper introduces an optimization model to maximize the deterministic equivalent of the random payoff function of a computer network administrator in defending the system against random attacks. By introducing new variables the transformed objective function becomes concave. A special optimization algorithm is developed which requires the computation of the unique solution of a single variable monotonic equation. The fourth paper, which is an invited book chapter, proposes a discrete-time stochastic control model to capture the process of finding the best current move of the defender. The defender's payoffs at each stage of the game depend on the attacker's and the defender's accumulative efforts and are considered random variables due to their uncertainty. Their certain equivalents can be approximated based on their first and second moments which is chosen as the cost functions of the dynamic system. An on-line, convergent, Scenarios based Proactive Defense (SPD) algorithm is developed based on Differential Dynamic Programming (DDP) to solve the associated optimal control problem.
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Hartigan, Patrick Francis. "Forecasting of radiowave attenuation on earth-space links." Thesis, Coventry University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.332325.
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Swanepoel, Paul. "A forecasting model for photovoltaic module energy production." Thesis, Nelson Mandela Metropolitan University, 2011. http://hdl.handle.net/10948/1420.
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Energy is of concern for governments and economies all over the world. As conventional methods of energy production are facing the prospect of depleting fossil fuel reserves, economies are facing energy risks. With this tension, various threats arise in terms of energy supply security. A shift from intensive fossil fuel consumption to alternative energy consumption combined with the calculated use of fossil fuels needs to be implemented. Using the energy radiated from the sun and converted to electricity through photovoltaic energy conversion is one of the alternative and renewable sources to address the limited fossil fuel dilemma. South Africa receives an abundance of sunlight irradiance, but limited knowledge of the implementation and possible energy yield of photovoltaic energy production in South Africa is available. Photovoltaic energy yield knowledge is vital in applications for farms, rural areas and remote transmitting devices where the construction of electricity grids are not cost effective. In this study various meteorological and energy parameters about photovoltaics were captured in Port Elizabeth (South Africa) and analyzed, with data being recorded every few seconds. A model for mean daily photovoltaic power output was developed and the relationships between the independent variables analyzed. A model was developed that can forecast mean daily photovoltaic power output using only temperature derived variables and time. The mean daily photovoltaic power model can then easily be used to forecast daily photovoltaic energy output using the number of sunlight seconds in a given day.
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Skouras, Konstantinos. "On the optimal performance of forecasting systems : the prequential approach." Thesis, University College London (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267943.
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