Relevant bibliographies by topics / Forecasting

Academic literature on the topic 'Forecasting'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Forecasting"

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Суворов, Anatoliy Suvorov, Ивантер, Viktor Ivantyer, Сутягин, and Valyeriy Sutyagin. "The Main Objectives and Principles of Socio-Economic Forecasting." Administration 3, no. 1 (March 17, 2015): 8–17. http://dx.doi.org/10.12737/8785.

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The methodological foundations of socio-economic forecasting are considered in this paper. The forecasting’s definition is given; the forecasting’s role and place in national economy regulation are considered. Forecasts types’ classification and the forecasting’s basic principles have been presented. Forecasting models’ structure and classification, as well as socio-economic forecasts’ elements and development stages have been considered.
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Zheng, Xiao Xia, and Fu Yang. "Research of Wind Speed and Wind Power Forecasting." Advanced Materials Research 347-353 (October 2011): 611–14. http://dx.doi.org/10.4028/www.scientific.net/amr.347-353.611.

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Wind power has entered a rapid progress stage. Due to the intermittency of wind energy and the non-linearity of power system, there exist many uncertain variables which should be considered in the wind power prediction. Accurate wind power forecastings are beneficial for wind plant operators, utility operators, and utility customers. The current forecasting methods include persistence method, physical method, statistical method, and the comprehensive one combing all the other methods. This paper provides a detail review on wind speed and wind power forecasting methods based on recent available published papers. Several forecasting models were discussed and a lot of researchers on the models, which have their own characteristics, were presented. An overview of comparative analysis of wind forecasting time scales is discussed as well.
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Rodrigues, Aaron. "Food Sales Forecasting Using Machine Learning Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 869–72. http://dx.doi.org/10.22214/ijraset.2021.38069.

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Abstract: Food sales forecasting is concerned with predicting future sales of food-related businesses such as supermarkets, grocery stores, restaurants, bakeries, and patisseries. Companies can reduce stocked and expired products within stores while also avoiding missing revenues by using accurate short-term sales forecasting. This research examines current machine learning algorithms for predicting food purchases. It goes over key design considerations for a data analyst working on food sales forecasting’s, such as the temporal granularity of sales data, the input variables to employ for forecasting sales, and the representation of the sales output variable. It also examines machine learning algorithms that have been used to anticipate food sales and the proper metrics for assessing their performance. Finally, it goes over the major problems and prospects for applied machine learning in the field of food sales forecasting. Keywords: Food, Demand forecasting, Machine learning, Regression, Timeseries forecasting, Sales prediction
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Reddy, Dr T. Koti. "Exchange Rate Forecasting." Indian Journal of Applied Research 1, no. 6 (October 1, 2011): 120–24. http://dx.doi.org/10.15373/2249555x/mar2012/41.

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Rathnayaka, R. M. Kapila Tharanga, D. M. K. N. Seneviratna, and Wei Jianguo. "Grey system based novel approach for stock market forecasting." Grey Systems: Theory and Application 5, no. 2 (August 3, 2015): 178–93. http://dx.doi.org/10.1108/gs-04-2015-0014.

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Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.
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BAŞER, Uğur, Mehmet BOZOĞLU, Nevra ALHAS EROĞLU, and Bakiye KILIÇ TOPUZ. "Forecasting Chestnut Production and Export of Turkey Using ARIMA Model." Turkish Journal of Forecasting 02, no. 2 (December 31, 2018): 27–33. http://dx.doi.org/10.34110/forecasting.482789.

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Arabi Belaghi, Reza, Minoo Aminnejad, and Özlem Gürünlü Alma. "Stock Market Prediction Using Nonparametric Fuzzy and Parametric GARCH Methods." Turkish Journal of Forecasting 02, no. 1 (September 1, 2018): 1–8. http://dx.doi.org/10.34110/forecasting.420126.

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Zewdie, Mulugeta Aklilu, Gebretsadik G. Wubit, and Amare W. Ayele. "G-STAR Model for Forecasting Space-Time Variation of Temperature in Northern Ethiopia." Turkish Journal of Forecasting 02, no. 1 (September 1, 2018): 9–19. http://dx.doi.org/10.34110/forecasting.437599.

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Kılıç Topuz, Bakiye, Mehmet Bozoğlu, Uğur Başer, and Nevra Alhas Eroğlu. "Forecasting of Apricot Production of Turkey by Using Box-Jenkins Method." Turkish Journal of Forecasting 02, no. 2 (December 31, 2018): 20–26. http://dx.doi.org/10.34110/forecasting.482914.

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MACİT, İrfan. "Estimating Risk Pressure Factor (RPF) with Artificial Neural Network (ANN) to Locate Search and Rescue (SAR) Team Station." Turkish Journal of Forecasting 03, no. 1 (August 31, 2019): 26–38. http://dx.doi.org/10.34110/forecasting.484765.

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Dissertations / Theses on the topic "Forecasting"

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Warren, Steven W. MATHEMATICAL MODELS WEATHER FORECASTING WEATHER PREDICTIONS MODELS REDUCTION PHYSICS OCEANOGRAPHY POWER REGRESSION ANALYSIS NAVY COMPARISON FORECASTING THESES. "Ensemble forecasting techniques in medium-range forecasting /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA267443.

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Thesis (M.S. in Meteorology and Physical Oceanography) Naval Postgraduate School, March 1993.
Thesis advisor(s): Wendell A. Nuss. "March 1993." Page 66 is missing (which includes Fig. 21 a-b). Bibliography: p. 109. Also available online.
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Warren, Steven W. "Ensemble forecasting techniques in medium-range forecasting." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/39902.

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Approved for public release; distribution is unlimited.
A continuing trend in numerical weather prediction (NWP) is the desire for reduced model forecast error. Developments in NWP such as advanced computing power and improved model physics and analysis methods have been successful in lowering error but are potentially limited The regression method of ensemble forecasting is used to further reduce mean forecast error when compared to individual model forecast performances. A statistical regression scheme is utilized to achieve an optimum combination fitting of the National Meteorological Center, the European Centre for Medium-Range Weather Forecasts, and the U.S. Navy Fleet Numerical Oceanography Center forecast models. The performance of the regression model is evaluated for 72-h and 108-h prediction cycles through statistical and subjective comparisons with the individual models and an equally weighted ensemble model at the surface and at 500 hPa. The regression model is shown to produce gains through the reduction of systematic error present in the individual model forecasts...
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Abdullah, Rozi. "Rainfall forecasting algorithms for real time flood forecasting." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296151.

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A fast catchment response usually leads to a shorter lag time, and under these conditions the forecast lead time obtained from a rainfall-runoff model or correlation between upstream and downstream flows may be infeasible for flood warning purposes. Additional lead time can be obtained from short-term quantitative rainfall forecasts that extend the flood warning time and increase the economic viability of a flood forecasting system. For this purpose algorithms which forecasts the quantitative rainfall amounts up to six hours ahead have been developed, based on lumped and distributed approaches. The lumped forecasting algorithm includes the essential features of storm dynamics such as rainband and raincell movements which are represented within the framework of a linear transfer function model. The dynamics of a storm are readily captured by radar data. A space-time rainfall model is used to generate synthetic radar data with known features, e.g. rainband and raincell velocities. This enables the algorithm to be assessed under ideal conditions, as errors are present in observed radar data. The transfer function algorithm can be summarised as follows. The dynamics of the rainbands and raincells are incorporated as inputs into the transfer function model. The algorithm employs simple spatial cross-correlation techniques to estimate the rainband and raincell velocities. The translated rainbands and raincells then form the auxiliary inputs to the transfer function. An optimal predictor based on minimum square error is then derived from the transfer function model, and its parameters are estimated from the auxiliary inputs and observed radar data in real-time using a recursive least squares algorithm. While the transfer-function algorithm forecasts areal rainfalls, a distributed approach which performs rainfall forecasting at a fine spatial resolution (referred to as the advection equation algorithm) is also evaluated in this thesis. The algorithm expresses the space-time rainfall on a Cartesian coordinate system via a partial differential advection equation. A simple explicit finite difference solution scheme is applied to the equation. A comparison of model parameter estimates is undertaken using a square root information filter data processing algorithm, and single-input single-output and multiple-input multiple-output least squares algorithms.
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Rasmussen, Steven R. "Forecasting 5" /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA304364.

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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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Minkah, Richard. "Forecasting volatility." Thesis, Uppsala University, Department of Mathematics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121079.

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Mayr, Johannes. "Forecasting Macroeconomic Aggregates." Diss., lmu, 2010. http://nbn-resolving.de/urn:nbn:de:bvb:19-111404.

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Noble, Christopher J. "Forecasting vortex filaments." Thesis, University of Canterbury. Physics, 1998. http://hdl.handle.net/10092/8165.

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The accuracy of stratospheric forecasts from the United Kingdom Meteorological Office's (UKMO) assimilation system in the Southern Hemisphere (SH) are studied primarily for a period in October 1994 and also February 1995. Conventional root mean square error (RMSE) calculations for different regions show that stratospheric forecasts are a large improvement over persistence in October 1994 (SH winter) even at five days but not so during February 1995 (SH summer). Systematic errors in the temperature and zonal wind fields were found to occur in relation with the stratopause and polar jet respectively. Studies also show that in general the vortex minimum temperature is forecast too cool and the maximum wind in the polar jet is forecast too strong. An advection scheme on specialised parcel location fields is used to study the differences in the meridional component of the wind vector with results indicating the forecast winds are highly consistent with the analysed winds even after five days in most cases. A back-trajectory mapping technique is employed to generate high-resolution maps of isentropic potential vorticity to permit the study of small-scale structure. The overall structure in a total hemisphere field produced from forecast winds is very similar to that from analysed winds even for filamentary structure near the polar vortex. Qualitative comparisons of aircraft measured tracer structure during the Airborne Southern Hemisphere Ozone Experiment (ASHOE) 1994 with structure from the high-resolution potential vorticity maps shows that large-scale features are represented well by the back-trajectory mapping technique with possibly less success for small-scale structure.
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CHRISTO, ELIANE DA SILVA. "REACTIVE POWER FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7622@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
No novo modelo do Setor Elétrico é essencial desenvolver novas técnicas que estimem valores futuros, a curto e longo-prazos, das potências ativa e reativa. Com base nisso, este trabalho tem por objetivo apresentar uma nova técnica de previsão horária de potência reativa a curto-prazo, por subestação, baseada na linearidade existente entre as potências ativa e reativa. O modelo proposto, denominado de Modelo Híbrido de Previsão de Reativo, é dividido em duas etapas: A primeira etapa é feita uma classificação dos dados através de uma rede neural não supervisionada Mapas Auto-Organizáveis de Kohonen (SOM); A segunda etapa, utiliza-se um modelo de defasagem distribuída auto-regressivo (ADL) com estimação de Mínimos Quadrados Reponderados Iterativamente (IRLS) acoplado a uma correção para autocorrelação serial dos resíduos - Método Iterativo de Cochrane-Orcutt. Este Modelo Híbrido tem como variável dependente a potência reativa, e como variáveis explicativas dados horários de potência ativa e reativa no instante atual e defasadas no tempo. A previsão de potência reativa a curto-prazo é dividida em in sample e em out of sample. A previsão out of sample é aplicada a períodos horários em até um mês à frente. O modelo proposto é aplicado aos dados de uma concessionária específica de Energia Elétrica e os resultados são comparados a um modelo de Regressão Dinâmica convencional e a um modelo de Redes Neurais Artificiais Feedforward de Múltiplas camadas (MLP) com um algoritmo de retropropagação do erro.
The forecasting of reactive and active power is an important tool in the monitoring of an Electrical Energy System. The main purpose of the present work is the development of a new short-term reactive power hourly forecast technique, which can be used at utility or substations levels. The proposed model, named A Hybrid Model for Reactive Forecasting, is divided in two stages. In the first stage, the active and reactive power data are classified by an unsupervised neural network - the Self-Organized Maps of Kohonen (SOM). In the second stage, a Autoregressive Distributed Lags Model (ADL) is used with its parameters estimated by an Iteratively Reweighted Least Square (IRLS). It also includes a correction lag structure for serial autocorrelation of the residuals as used in the Cochrane-Orcutt formulation. The short term reactive power forecasting is divided in in sample and out of sample. The out of sample forecast is applied to hourly periods until one month ahead. The proposed model is applied to real data of one substation and the results are compared with two other approaches, a conventional Dynamic Regression and a Feedforward Multi-layer Perceptron (MLP) Artificial Neural Network model.
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Binter, Roman. "Applied probabilistic forecasting." Thesis, London School of Economics and Political Science (University of London), 2012. http://etheses.lse.ac.uk/559/.

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In any actual forecast, the future evolution of the system is uncertain and the forecasting model is mathematically imperfect. Both, ontic uncertainties in the future (due to true stochasticity) and epistemic uncertainty of the model (reflecting structural imperfections) complicate the construction and evaluation of probabilistic forecast. In almost all nonlinear forecast models, the evolution of uncertainty in time is not tractable analytically and Monte Carlo approaches (”ensemble forecasting”) are widely used. This thesis advances our understanding of the construction of forecast densities from ensembles, the evolution of the resulting probability forecasts and methods of establishing skill (benchmarks). A novel method of partially correcting the model error is introduced and shown to outperform a competitive approach. The properties of Kernel dressing, a method of transforming ensembles into probability density functions, are investigated and the convergence of the approach is illustrated. A connection between forecasting and Information theory is examined by demonstrating that Kernel dressing via minimization of Ignorance implicitly leads to minimization of Kulback-Leibler divergence. The Ignorance score is critically examined in the context of other Information theory measures. The method of Dynamic Climatology is introduced as a new approach to establishing skill (benchmarking). Dynamic Climatology is a new, relatively simple, nearest neighbor based model shown to be of value in benchmarking of global circulation models of the ENSEMBLES project. ENSEMBLES is a project funded by the European Union bringing together all major European weather forecasting institutions in order to develop and test state-of-the-art seasonal weather forecasting models. Via benchmarking the seasonal forecasts of the ENSEMBLES models we demonstrate that Dynamic Climatology can help us better understand the value and forecasting performance of large scale circulation models. Lastly, a new approach to correcting (improving) imperfect model is presented, an idea inspired by [63]. The main idea is based on a two-stage procedure where a second stage ‘corrective’ model iteratively corrects systematic parts of forecasting errors produced by a first stage ‘core’ model. The corrector is of an iterative nature so that at a given time t the core model forecast is corrected and then used as an input into the next iteration of the core model to generate a time t + 1 forecast. Using two nonlinear systems we demonstrate that the iterative corrector is superior to alternative approaches based on direct (non-iterative) forecasts. While the choice of the corrector model class is flexible, we use radial basis functions. Radial basis functions are frequently used in statistical learning and/or surface approximations and involve a number of computational aspects which we discuss in some detail.
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Books on the topic "Forecasting"

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McAuliffe, Bill. Forecasting. Mankato, MN: Creative Education, 2010.

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Fildes, Robert, and P. Allen. Forecasting. 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications Ltd, 2011. http://dx.doi.org/10.4135/9781446261668.

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Hirschmann, Kris. Forecasting! Edina, MN: ABDO Pub., 2008.

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Allen, P. Geoffrey, and Robert Fildes. Forecasting. Los Angeles, Calif: SAGE, 2011.

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National Research Council (U.S.). Transportation Research Board. Transportation Data, Economics, and Forecasting Section. and National Research Council (U.S.). Transportation Research Board. Meeting, eds. Forecasting. Washington, D.C: Transportation Research Board, National Research Council, 1989.

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D, Lawrence Kenneth, Geurts Michael, and Guerard John B, eds. Forecasting. Greenwich, Conn: JAI Press, 1998.

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Latham, Donna. Weather forecasting. Glenview, Ill: Pearson/Scott Foresman, 2010.

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Centre, Nottingham Fashion. Trend forecasting. Nottingham: Nottingham Fashion Centre, 1999.

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Molnar, Alan T. Economic forecasting. New York: Nova Science Publishers, 2010.

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Carnot, Nicolas, Vincent Koen, and Bruno Tissot. Economic Forecasting. London: Palgrave Macmillan UK, 2005. http://dx.doi.org/10.1057/9780230005815.

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Book chapters on the topic "Forecasting"

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Khan, Arshad. "Forecasting." In Jumpstart Tableau, 331–36. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1934-8_36.

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Bronzite, Michael. "Forecasting." In System Development, 83–99. London: Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0469-8_6.

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Christou, Ioannis T. "Forecasting." In Quantitative Methods in Supply Chain Management, 139–202. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-766-2_2.

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Leininger, Arndt. "Forecasting." In Handbuch Methoden der Politikwissenschaft, 651–69. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-16936-7_36.

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Leininger, Arndt. "Forecasting." In Handbuch Organisationssoziologie, 1–20. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-16937-4_36-1.

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Leininger, Arndt. "Forecasting." In Handbuch Organisationssoziologie, 1–20. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-16937-4_36-2.

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Thomopoulos, Nick T. "Forecasting." In Elements of Manufacturing, Distribution and Logistics, 1–27. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26862-0_1.

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Runkler, Thomas A. "Forecasting." In Data Analytics, 79–83. Wiesbaden: Vieweg+Teubner Verlag, 2012. http://dx.doi.org/10.1007/978-3-8348-2589-6_7.

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Swift, Louise. "Forecasting." In Mathematics and Statistics for Business, Management and Finance, 864–904. London: Macmillan Education UK, 1997. http://dx.doi.org/10.1007/978-1-349-25273-2_19.

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De Gooijer, Jan G. "Forecasting." In Elements of Nonlinear Time Series Analysis and Forecasting, 391–437. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-43252-6_10.

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Conference papers on the topic "Forecasting"

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Shetty, Sachin, Valentina Gori, Gianni Bagni, and Giacomo Veneri. "Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application." In International Conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2023. http://dx.doi.org/10.3390/engproc2023039096.

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Lanzilotta, Bibiana, Gabriela Mordecki, Pablo Tapie, and Joaquín Torres. "Uncertainty and Business Cycle: An Empirical Analysis for Uruguay." In International Conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2023. http://dx.doi.org/10.3390/engproc2023039097.

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Tabib, Mandar, Kristoffer Skare, Endre Bruaset, and Adil Rasheed. "Data-Driven Spatio-Temporal Modelling and Optimal Sensor Placement for a Digital Twin Set-Up." In International Conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2023. http://dx.doi.org/10.3390/engproc2023039098.

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Oladeji, Jonathan D., Benita G. Zulch, and Joseph A. Yacim. "Predictive Accuracy of Logit Regression for Data-Scarce Developing Markets: A Nigeria and South Africa Study." In International Conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2023. http://dx.doi.org/10.3390/engproc2023039100.

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Mononen, Asko, Ari Alamäki, Janne Kauttonen, Aarne Klemetti, Anu Passi-Rauste, and Harri Ketamo. "Forecasted Self: AI-Based Careerbot-Service Helping Students with Job Market Dynamics." In International Conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2023. http://dx.doi.org/10.3390/engproc2023039099.

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Valenzuela, Olga, Fernando Rojas, Luis Javier Herrera, Hector Pomares, and Ignacio Rojas. "New Developments in Time Series and Forecasting, ITISE-2023." In International Conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2023. http://dx.doi.org/10.3390/engproc2023039101.

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Edwards, Samuel J., Matthew D. Collette, and Armin W. Troesch. "Extreme Characteristics of a Stochastic Non-Stationary Duffing Oscillator." In International Conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2023. http://dx.doi.org/10.3390/engproc2023039102.

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Alander, Juha, Lauri Honkasilta, and Kalle Saastamoinen. "Simulating the Aerial Ballet: The Dance of Fire-Fighting Planes and Helicopters." In International conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2024. http://dx.doi.org/10.3390/engproc2024068054.

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Ahmad, Farooq, Livio Finos, and Mariangela Guidolin. "Modeling the Future of Hydroelectric Power: A Cross-Country Study." In International conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2024. http://dx.doi.org/10.3390/engproc2024068056.

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Montante, Carmin, and Clemente Hernandez-Rodriguez. "Evaluation of Economic Interventions in Economic Blocks during an Economic and Sanitary Crisis." In International conference on Time Series and Forecasting. Basel Switzerland: MDPI, 2024. http://dx.doi.org/10.3390/engproc2024068055.

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Reports on the topic "Forecasting"

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Cook, Steve. Directional Forecasting, Forecasting Accuracy and Making Profits. Bristol, UK: The Economics Network, September 2014. http://dx.doi.org/10.53593/n2703a.

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Stock, James, and Mark Watson. Forecasting Inflation. Cambridge, MA: National Bureau of Economic Research, March 1999. http://dx.doi.org/10.3386/w7023.

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Xiong, Yingge, Jon Fricker, and Kevin McNamara. Socioeconomic Forecasting. Purdue University, December 2012. http://dx.doi.org/10.5703/1288284314664.

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Rosenzweig, Mark, and Christopher Udry. Forecasting Profitability. Cambridge, MA: National Bureau of Economic Research, August 2013. http://dx.doi.org/10.3386/w19334.

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Hyman, Ellis, Roger Shi, Mark Czarnaski, and Sethu Raman. Atmospheric Forecasting. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada384012.

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Andersen, Torben, Tim Bollerslev, Peter Christoffersen, and Francis Diebold. Volatility Forecasting. Cambridge, MA: National Bureau of Economic Research, March 2005. http://dx.doi.org/10.3386/w11188.

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Schwarz, Kurt F., IV Brooks, and Thomas L. Forecasting Contracting Workload. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada211935.

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8

Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200431-2.

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Abstract:
This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressions can exploit the information content of the macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
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Roberts, Benedict C. Multiple Forecasting Techniques. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada230600.

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10

Garegnani, Lorena, and Maximiliano Gómez Aguirre. Forecasting Inflation in Argentina. Inter-American Development Bank, June 2018. http://dx.doi.org/10.18235/0001160.

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