Rozprawy doktorskie na temat „Time series aggregation”
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Tripodis, Georgios. "Heterogeneity and aggregation in seasonal time series". Thesis, London School of Economics and Political Science (University of London), 2007. http://etheses.lse.ac.uk/2933/.
Pełny tekst źródłaLin, Shu-Chin. "Aggregation and time series implications of state-dependent consumption /". free to MU campus, to others for purchase, 1996. http://wwwlib.umi.com/cr/mo/fullcit?p9737881.
Pełny tekst źródłaSariaslan, Nazli. "The Effect Of Temporal Aggregation On Univariate Time Series Analysis". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612528/index.pdf.
Pełny tekst źródłaGehman, Andrew J. "The Effects of Spatial Aggregation on Spatial Time Series Modeling and Forecasting". Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/382669.
Pełny tekst źródłaPh.D.
Spatio-temporal data analysis involves modeling a variable observed at different locations over time. A key component of space-time modeling is determining the spatial scale of the data. This dissertation addresses the following three questions: 1) How does spatial aggregation impact the properties of the variable and its model? 2) What spatial scale of the data produces more accurate forecasts of the aggregate variable? 3) What properties lead to the smallest information loss due to spatial aggregation? Answers to these questions involve a thorough examination of two common space-time models: the STARMA and GSTARMA models. These results are helpful to researchers seeking to understand the impact of spatial aggregation on temporal and spatial correlation as well as to modelers interested in determining a spatial scale for the data. Two data examples are included to illustrate the findings, and they concern states' annual labor force totals and monthly burglary counts for police districts in the city of Philadelphia.
Temple University--Theses
Kim, Hang. "TIME SERIES BLOCK BOOTSTRAP APPLICATION AND EFFECT OF AGGREGATION AND SYSTEMATIC SAMPLING". Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/490644.
Pełny tekst źródłaPh.D.
In this dissertation, we review the basic properties of the bootstrap and time series application. Then we apply parametric bootstrap on three simulated normal i.i.d. samples and nonparametric bootstrap on four real life financial returns. Among the time series bootstrap methods, we look into the specific method called block bootstrap and investigate the block length consideration to properly select a suitable block size for AR(1) model. We propose a new rule of blocking named as Combinatorially-Augmented Block Bootstrap(CABB). We compare the existing block bootstrap and CABB method using the simulated i.i.d. samples, AR(1) time series, and the real life examples. Both methods perform equally well in estimating AR(1) coefficients. CABB produces a smaller standard deviation based on our simulated and empirical studies. We study two procedures of collecting time series, (i) aggregation of a flow variable and (ii) systematic sampling of a stock variable. In these two procedures, we derive theorems that calculate exact equations for $m$ aggregated and $m^{th}$ systematically sampled series of the original AR(1) model. We evaluate the performance of block bootstrap estimation of the parameters of ARMA(1,1) and AR(1) model using aggregated and systematically sampled series. Simulation and real data analyses show that in some cases, the performance of the estimation based on the block bootstrap method for the MA(1) parameter of the ARMA(1,1) model in aggregated series is better than the one without using bootstrap. In an extreme case of stock price movement, which is close to a random walk, the block bootstrap estimate using systematically sampled series is closer to the true parameter, defined as the parameter calculated by the theorem. Specifically, the block bootstrap estimate of the parameter of AR(1) model using the systematically sampled series is closer to phi(n) than that based on the MLE for the AR(1) model. Future research problems include theoretical investigation of CABB, effectiveness of block bootstrap in other time series analyses such as nonlinear or VAR.
Temple University--Theses
APRAEZ, CESAR DAVID REVELO. "A HYBRID NEURO- EVOLUTIONARY APPROACH FOR DYNAMIC WEIGHTED AGGREGATION OF TIME SERIES FORECASTERS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36950@1.
Pełny tekst źródłaCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Estudos empíricos na área de séries temporais indicam que combinar modelos preditivos, originados a partir de diferentes técnicas de modelagem, levam a previsões consensuais superiores, em termos de acurácia, às previsões individuais dos modelos envolvidos na combinação. No presente trabalho é apresentada uma metodologia de combinação convexa de modelos estatísticos de previsão, cujo sucesso depende da forma como os pesos de combinação de cada modelo são estimados. Uma Rede Neural Artificial Perceptron Multi-camada (Multilayer Perceptron - MLP) é utilizada para gerar dinamicamente vetores de pesos ao longo do horizonte de previsão, sendo estes dependentes da contribuição individual de cada previsor observada nos dados históricos da série. O ajuste dos parâmetros da rede MLP é efetuado através de um algoritmo de treinamento híbrido, que integra técnicas de busca global, baseadas em computação evolucionária, junto com o algoritmo de busca local backpropagation, de modo a otimizar de forma simultânea tanto os pesos quanto a arquitetura da rede, visando, assim, a gerar de forma automática um modelo de ponderação dinâmica de previsores de alto desempenho. O modelo proposto, batizado de Neural Expert Weighting - Genetic Algorithm (NEW-GA), foi avaliado em diversos experimentos comparativos com outros modelos de ponderação de previsores, assim como também com os modelos individuais envolvidos na combinação, contemplando 15 séries temporais divididas em dois estudos de casos: séries de derivados de petróleo e séries da versão reduzida da competição NN3, uma competição entre metodologias de previsão, com maior ênfase nos modelos baseados em Redes Neurais. Os resultados demonstraram o potencial do NEWGA em fornecer modelos acurados de previsão de séries temporais.
Empirical studies on time series indicate that the combination of forecasting models, generated from different modeling techniques, leads to higher consen+sus forecasts, in terms of accuracy, than the forecasts of individual models involved in the combination scheme. In this work, we present a methodology for convex combination of statistical forecasting models, whose success depends on how the combination weights of each model are estimated. An Artificial Neural Network Multilayer Perceptron (MLP) is used to generate dynamically weighting vectors over the forecast horizon, being dependent on the individual contribution of each forecaster observed over historical data series. The MLP network parameters are adjusted via a hybrid training algorithm that integrates global search techniques, based on evolutionary computation, along with the local search algorithm backpropagation, in order to optimize simultaneously both weights and network architecture. This approach aims to automatically generate a dynamic weighted forecast aggregation model with high performance. The proposed model, called Neural Expert Weighting - Genetic Algorithm (NEW-GA), was com- pared with other forecaster combination models, as well as with the individual models involved in the combination scheme, comprising 15 time series divided into two case studies: Petroleum Products and the reduced set of NN3 forecasting competition, a competition between forecasting methodologies, with greater emphasis on models based on neural networks. The results obtained demonstrated the potential of NEW-GA in providing accurate models for time series forecasting.
Weiss, Christoph. "Essays in hierarchical time series forecasting and forecast combination". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274757.
Pełny tekst źródłaDoell, Christoph [Verfasser]. "Methods for Multivariate Time-Series Classification on Brain Data : Aggregation, Stratification and Neural Network Models / Christoph Doell". Konstanz : KOPS Universität Konstanz, 2021. http://d-nb.info/1232176648/34.
Pełny tekst źródłaBahl, Björn [Verfasser], André [Akademischer Betreuer] Bardow i Francois [Akademischer Betreuer] Marechal. "Optimization-based synthesis of large-scale energy systems by time-series aggregation / Björn Bahl ; André Bardow, Francois Marechal". Aachen : Universitätsbibliothek der RWTH Aachen, 2018. http://d-nb.info/1186069260/34.
Pełny tekst źródłaLee, Bu Hyoung. "The use of temporally aggregated data on detecting a structural change of a time series process". Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/375511.
Pełny tekst źródłaPh.D.
A time series process can be influenced by an interruptive event which starts at a certain time point and so a structural break in either mean or variance may occur before and after the event time. However, the traditional statistical tests of two independent samples, such as the t-test for a mean difference and the F-test for a variance difference, cannot be directly used for detecting the structural breaks because it is almost certainly impossible that two random samples exist in a time series. As alternative methods, the likelihood ratio (LR) test for a mean change and the cumulative sum (CUSUM) of squares test for a variance change have been widely employed in literature. Another point of interest is temporal aggregation in a time series. Most published time series data are temporally aggregated from the original observations of a small time unit to the cumulative records of a large time unit. However, it is known that temporal aggregation has substantial effects on process properties because it transforms a high frequency nonaggregate process into a low frequency aggregate process. In this research, we investigate the effects of temporal aggregation on the LR test and the CUSUM test, through the ARIMA model transformation. First, we derive the proper transformation of ARIMA model orders and parameters when a time series is temporally aggregated. For the LR test for a mean change, its test statistic is associated with model parameters and errors. The parameters and errors in the statistic should be changed when an AR(p) process transforms upon the mth order temporal aggregation to an ARMA(P,Q) process. Using the property, we propose a modified LR test when a time series is aggregated. Through Monte Carlo simulations and empirical examples, we show that the aggregation leads the null distribution of the modified LR test statistic being shifted to the left. Hence, the test power increases as the order of aggregation increases. For the CUSUM test for a variance change, we show that two aggregation terms will appear in the test statistic and have negative effects on test results when an ARIMA(p,d,q) process transforms upon the mth order temporal aggregation to an ARIMA(P,d,Q) process. Then, we propose a modified CUSUM test to control the terms which are interpreted as the aggregation effects. Through Monte Carlo simulations and empirical examples, the modified CUSUM test shows better performance and higher test powers to detect a variance change in an aggregated time series than the original CUSUM test.
Temple University--Theses
Sharif, Abbass. "Visual Data Mining Techniques for Functional Actigraphy Data: An Object-Oriented Approach in R". DigitalCommons@USU, 2012. https://digitalcommons.usu.edu/etd/1394.
Pełny tekst źródłaSilvestrini, Andrea. "Essays on aggregation and cointegration of econometric models". Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210304.
Pełny tekst źródłaChapter 1 surveys the econometric methodology of temporal aggregation for a wide range of univariate and multivariate time series models.
A unified overview of temporal aggregation techniques for this broad class of processes is presented in the first part of the chapter and the main results are summarized. In each case, assuming to know the underlying process at the disaggregate frequency, the aim is to find the appropriate model for the aggregated data. Additional topics concerning temporal aggregation of ARIMA-GARCH models (see Drost and Nijman, 1993) are discussed and several examples presented. Systematic sampling schemes are also reviewed.
Multivariate models, which show interesting features under temporal aggregation (Breitung and Swanson, 2002, Marcellino, 1999, Hafner, 2008), are examined in the second part of the chapter. In particular, the focus is on temporal aggregation of VARMA models and on the related concept of spurious instantaneous causality, which is not a time series property invariant to temporal aggregation. On the other hand, as pointed out by Marcellino (1999), other important time series features as cointegration and presence of unit roots are invariant to temporal aggregation and are not induced by it.
Some empirical applications based on macroeconomic and financial data illustrate all the techniques surveyed and the main results.
Chapter 2 is an attempt to monitor fiscal variables in the Euro area, building an early warning signal indicator for assessing the development of public finances in the short-run and exploiting the existence of monthly budgetary statistics from France, taken as "example country".
The application is conducted focusing on the cash State deficit, looking at components from the revenue and expenditure sides. For each component, monthly ARIMA models are estimated and then temporally aggregated to the annual frequency, as the policy makers are interested in yearly predictions.
The short-run forecasting exercises carried out for years 2002, 2003 and 2004 highlight the fact that the one-step-ahead predictions based on the temporally aggregated models generally outperform those delivered by standard monthly ARIMA modeling, as well as the official forecasts made available by the French government, for each of the eleven components and thus for the whole State deficit. More importantly, by the middle of the year, very accurate predictions for the current year are made available.
The proposed method could be extremely useful, providing policy makers with a valuable indicator when assessing the development of public finances in the short-run (one year horizon or even less).
Chapter 3 deals with the issue of forecasting contemporaneous time series aggregates. The performance of "aggregate" and "disaggregate" predictors in forecasting contemporaneously aggregated vector ARMA (VARMA) processes is compared. An aggregate predictor is built by forecasting directly the aggregate process, as it results from contemporaneous aggregation of the data generating vector process. A disaggregate predictor is a predictor obtained from aggregation of univariate forecasts for the individual components of the data generating vector process.
The econometric framework is broadly based on Lütkepohl (1987). The necessary and sufficient condition for the equality of mean squared errors associated with the two competing methods in the bivariate VMA(1) case is provided. It is argued that the condition of equality of predictors as stated in Lütkepohl (1987), although necessary and sufficient for the equality of the predictors, is sufficient (but not necessary) for the equality of mean squared errors.
Furthermore, it is shown that the same forecasting accuracy for the two predictors can be achieved using specific assumptions on the parameters of the VMA(1) structure.
Finally, an empirical application that involves the problem of forecasting the Italian monetary aggregate M1 on the basis of annual time series ranging from 1948 until 1998, prior to the creation of the European Economic and Monetary Union (EMU), is presented to show the relevance of the topic. In the empirical application, the framework is further generalized to deal with heteroskedastic and cross-correlated innovations.
Chapter 4 deals with a cointegration analysis applied to the empirical investigation of fiscal sustainability. The focus is on a particular country: Poland. The choice of Poland is not random. First, the motivation stems from the fact that fiscal sustainability is a central topic for most of the economies of Eastern Europe. Second, this is one of the first countries to start the transition process to a market economy (since 1989), providing a relatively favorable institutional setting within which to study fiscal sustainability (see Green, Holmes and Kowalski, 2001). The emphasis is on the feasibility of a permanent deficit in the long-run, meaning whether a government can continue to operate under its current fiscal policy indefinitely.
The empirical analysis to examine debt stabilization is made up by two steps.
First, a Bayesian methodology is applied to conduct inference about the cointegrating relationship between budget revenues and (inclusive of interest) expenditures and to select the cointegrating rank. This task is complicated by the conceptual difficulty linked to the choice of the prior distributions for the parameters relevant to the economic problem under study (Villani, 2005).
Second, Bayesian inference is applied to the estimation of the normalized cointegrating vector between budget revenues and expenditures. With a single cointegrating equation, some known results concerning the posterior density of the cointegrating vector may be used (see Bauwens, Lubrano and Richard, 1999).
The priors used in the paper leads to straightforward posterior calculations which can be easily performed.
Moreover, the posterior analysis leads to a careful assessment of the magnitude of the cointegrating vector. Finally, it is shown to what extent the likelihood of the data is important in revising the available prior information, relying on numerical integration techniques based on deterministic methods.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished
Hellström, Jörgen. "Count data modelling and tourism demand". Doctoral thesis, Umeå universitet, Institutionen för nationalekonomi, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-82168.
Pełny tekst źródłaHärtill 4 uppsatser.
digitalisering@umu
Inersjö, Adam. "Transformation of Time-based Sensor Data to Material Quality Data in Stainless Steel Production". Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-414802.
Pełny tekst źródłaKvalitetssäkring av rostfritt stål produktion kräver stora mängder av sensordata för att övervaka processtegen. Digitalisering av produktionen skulle ge större kontroll för att både bedöma och öka kvaliteten på slutprodukterna. Vid Outokumpu Avesta Works skapas sensordata vid kontinuerlig bearbetning av stålband utan att datan sammankopplas till enskilda band, trots att denna sammankoppling krävs för att uppnå löftena som digitaliseringens ger. I detta projekt analyserades tidsseriedata från 12 sensorer vid den kontinuerliga bearbetningen av band och fyra alternativa metoder för att sammankoppla sensordatan till stålband presenterades. En metod som byggde på tidsserier med positionsvärden bedömdes vara mest passande för sensordatan och valdes för implementation över andra metoder som byggde på tidsserieanalys av själva sensordatan. Evaluering av den valda metoden visade att den kunde sammankoppla sensordata till 98.10 % av ståldbanden, något lägre än kravet på 99 % korrekthet. På grund av att skapandet av tidsserierna med positionsvärden tog lika lång tid oberoende av antalet sensorer så förbättrades bearbetningstiden desto fler sensorer som bearbetades. För bearbetning av 24 timmar av sensordata låg median bearbetningstiden på mindre än 20 sekunder per sensor när åtta eller fler sensorer bearbetades tillsammans. Prestandan för bearbetning av färre än fyra sensorer var inte lilka bra och kräver ytterliga optimering för att nå kravet på 30 sekunder per sensor. Fastän kraven på metoden inte uppnåddes till fullo kan den implementerade metoden ändå användas på historisk data för att främja kvalitetsbedömning av rostfria stålband.
Gaillard, Pierre. "Contributions à l’agrégation séquentielle robuste d’experts : Travaux sur l’erreur d’approximation et la prévision en loi. Applications à la prévision pour les marchés de l’énergie". Thesis, Paris 11, 2015. http://www.theses.fr/2015PA112133/document.
Pełny tekst źródłaWe are interested in online forecasting of an arbitrary sequence of observations. At each time step, some experts provide predictions of the next observation. Then, we form our prediction by combining the expert forecasts. This is the setting of online robust aggregation of experts. The goal is to ensure a small cumulative regret. In other words, we want that our cumulative loss does not exceed too much the one of the best expert. We are looking for worst-case guarantees: no stochastic assumption on the data to be predicted is made. The sequence of observations is arbitrary. A first objective of this work is to improve the prediction accuracy. We investigate several possibilities. An example is to design fully automatic procedures that can exploit simplicity of the data whenever it is present. Another example relies on working on the expert set so as to improve its diversity. A second objective of this work is to produce probabilistic predictions. We are interested in coupling the point prediction with a measure of uncertainty (i.e., interval forecasts,…). The real world applications of the above setting are multiple. Indeed, very few assumptions are made on the data. Besides, online learning that deals with data sequentially is crucial to process big data sets in real time. In this thesis, we carry out for EDF several empirical studies of energy data sets and we achieve good forecasting performance
Sànchez, Pérez Andrés. "Agrégation de prédicteurs pour des séries temporelles, optimalité dans un contexte localement stationnaire". Thesis, Paris, ENST, 2015. http://www.theses.fr/2015ENST0051/document.
Pełny tekst źródłaThis thesis regroups our results on dependent time series prediction. The work is divided into three main chapters where we tackle different problems. The first one is the aggregation of predictors of Causal Bernoulli Shifts using a Bayesian approach. The second one is the aggregation of predictors of what we define as sub-linear processes. Locally stationary time varying autoregressive processes receive a particular attention; we investigate an adaptive prediction scheme for them. In the last main chapter we study the linear regression problem for a general class of locally stationary processes
Sànchez, Pérez Andrés. "Agrégation de prédicteurs pour des séries temporelles, optimalité dans un contexte localement stationnaire". Electronic Thesis or Diss., Paris, ENST, 2015. http://www.theses.fr/2015ENST0051.
Pełny tekst źródłaThis thesis regroups our results on dependent time series prediction. The work is divided into three main chapters where we tackle different problems. The first one is the aggregation of predictors of Causal Bernoulli Shifts using a Bayesian approach. The second one is the aggregation of predictors of what we define as sub-linear processes. Locally stationary time varying autoregressive processes receive a particular attention; we investigate an adaptive prediction scheme for them. In the last main chapter we study the linear regression problem for a general class of locally stationary processes
Shirizadeh, Ghezeljeh Behrang. "Reaching carbon neutrality in France by 2050 : optimal choice of energy sources, carriers and storage options". Electronic Thesis or Diss., Paris, EHESS, 2021. http://www.theses.fr/2021EHES0013.
Pełny tekst źródłaTo stay in line with 1.5°C of global warming, the French government has adopted the target of net zero greenhouse gas emissions by 2050. The main greenhouse gas being carbon dioxide, and the majority of its emissions being due to energy combustion, this dissertation focuses on reaching carbon-neutrality in French energy-related CO2 emissions by 2050. This thesis dissertation aims to study the relative role of different low-carbon mitigation options in the energy sector in reaching carbon-neutrality. More precisely, this thesis first studies the French power sector, first in a fully renewable power system, and second in a power system containing other mitigation options i.e. nuclear energy and carbon capture and storage. I study the impact of uncertainties related to cost development of renewables and storage options and address the robustness of a fully renewable power system to cost uncertainties. Later, adding other low-carbon mitigation options in the power sector, I analyze the relative role of different low-carbon options. Similarly, to incentivize the investments in variable renewable energy sources such as wind and solar power, I study the investment risk related to the price and volume volatility of renewable electricity technologies, and the performance of different public policy support schemes. The analysis in this thesis goes beyond the electricity system and it also considers the whole energy system in the presence of sector-coupling. During this thesis, I have developed a family of models optimizing dispatch and investment to answer different questions regarding the French energy transition. These models minimize the cost of the considered system (electricity system or the whole energy system) by satisfying the supply/demand equilibrium at each hour over at least one year, respecting the main technical and operational, resource related and land-use constraints. Thus, both short-term and long-term variability of renewable energy sources are taken into account. Using these models, I address the questions raised above. These models are not used to find a single optimal solution, but several optimal solutions depending on different weather, cost, energy demand and technology availability scenarios. Therefore, the importance of robustness to the uncertainties is at the center of the used methodology beside optimality. The findings of my thesis show that renewable energy supply sources are the main enablers of reaching carbon neutrality in a cost-effective way, no matter the considered energy system; either only electricity or the whole energy system. While the elimination of nuclear power barely increases the cost of a carbon-neutral energy system, the elimination of renewables is associated with high inefficiencies both from the cost and emission points of view. In fact, if renewable gas is not available, even a social cost of carbon of €500/tCO2 will not be enough to reach carbon-neutrality. This is partially due to the negative emissions that it can provide once combined with carbon capture and storage, and partially due to the cost-optimality of renewable gas-fired internal combustion engines in reaching carbon-neutrality in the transport sector. This dissertation has several important policy-related messages; however, the central one is that reaching carbon-neutrality for the lowest cost requires a highly renewable energy system. Therefore, if we are to prioritize investment in low-carbon options, renewable gas and electricity technologies are of the highest importance
Lin, Hsing-Chung, i 林信忠. "Contemporaneous on aggregation of stationary vector time series". Thesis, 1994. http://ndltd.ncl.edu.tw/handle/93708887088111805070.
Pełny tekst źródłaSebatjane, Phuti. "Understanding patterns of aggregation in count data". Diss., 2016. http://hdl.handle.net/10500/22067.
Pełny tekst źródłaStatistics
M.Sc. (Statistics)
Saker, Halima. "Segmentation of Heterogeneous Multivariate Genome Annotation Data". 2021. https://ul.qucosa.de/id/qucosa%3A75914.
Pełny tekst źródłaAlves, Pedro Miguel Carregueiro Jordão. "The effect of serial correlation in time-aggregation of annual sharpe ratios from monthly data". Master's thesis, 2018. http://hdl.handle.net/10362/32318.
Pełny tekst źródłaALAIMO, LEONARDO. "Complexity of social phenomena: measurements, analysis, representations and synthesis". Doctoral thesis, 2020. http://hdl.handle.net/11573/1360691.
Pełny tekst źródłaRiba, Evans Mogolo. "Exploring advanced forecasting methods with applications in aviation". Diss., 2021. http://hdl.handle.net/10500/27410.
Pełny tekst źródłaMore time series forecasting methods were researched and made available in recent years. This is mainly due to the emergence of machine learning methods which also found applicability in time series forecasting. The emergence of a variety of methods and their variants presents a challenge when choosing appropriate forecasting methods. This study explored the performance of four advanced forecasting methods: autoregressive integrated moving averages (ARIMA); artificial neural networks (ANN); support vector machines (SVM) and regression models with ARIMA errors. To improve their performance, bagging was also applied. The performance of the different methods was illustrated using South African air passenger data collected for planning purposes by the Airports Company South Africa (ACSA). The dissertation discussed the different forecasting methods at length. Characteristics such as strengths and weaknesses and the applicability of the methods were explored. Some of the most popular forecast accuracy measures were discussed in order to understand how they could be used in the performance evaluation of the methods. It was found that the regression model with ARIMA errors outperformed all the other methods, followed by the ARIMA model. These findings are in line with the general findings in the literature. The ANN method is prone to overfitting and this was evident from the results of the training and the test data sets. The bagged models showed mixed results with marginal improvement on some of the methods for some performance measures. It could be concluded that the traditional statistical forecasting methods (ARIMA and the regression model with ARIMA errors) performed better than the machine learning methods (ANN and SVM) on this data set, based on the measures of accuracy used. This calls for more research regarding the applicability of the machine learning methods to time series forecasting which will assist in understanding and improving their performance against the traditional statistical methods
Die afgelope tyd is verskeie tydreeksvooruitskattingsmetodes ondersoek as gevolg van die ontwikkeling van masjienleermetodes met toepassings in die vooruitskatting van tydreekse. Die nuwe metodes en hulle variante laat ʼn groot keuse tussen vooruitskattingsmetodes. Hierdie studie ondersoek die werkverrigting van vier gevorderde vooruitskattingsmetodes: outoregressiewe, geïntegreerde bewegende gemiddeldes (ARIMA), kunsmatige neurale netwerke (ANN), steunvektormasjiene (SVM) en regressiemodelle met ARIMA-foute. Skoenlussaamvoeging is gebruik om die prestasie van die metodes te verbeter. Die prestasie van die vier metodes is vergelyk deur hulle toe te pas op Suid-Afrikaanse lugpassasiersdata wat deur die Suid-Afrikaanse Lughawensmaatskappy (ACSA) vir beplanning ingesamel is. Hierdie verhandeling beskryf die verskillende vooruitskattingsmetodes omvattend. Sowel die positiewe as die negatiewe eienskappe en die toepasbaarheid van die metodes is uitgelig. Bekende prestasiemaatstawwe is ondersoek om die prestasie van die metodes te evalueer. Die regressiemodel met ARIMA-foute en die ARIMA-model het die beste van die vier metodes gevaar. Hierdie bevinding strook met dié in die literatuur. Dat die ANN-metode na oormatige passing neig, is deur die resultate van die opleidings- en toetsdatastelle bevestig. Die skoenlussamevoegingsmodelle het gemengde resultate opgelewer en in sommige prestasiemaatstawwe vir party metodes marginaal verbeter. Op grond van die waardes van die prestasiemaatstawwe wat in hierdie studie gebruik is, kan die gevolgtrekking gemaak word dat die tradisionele statistiese vooruitskattingsmetodes (ARIMA en regressie met ARIMA-foute) op die gekose datastel beter as die masjienleermetodes (ANN en SVM) presteer het. Dit dui op die behoefte aan verdere navorsing oor die toepaslikheid van tydreeksvooruitskatting met masjienleermetodes om hul prestasie vergeleke met dié van die tradisionele metodes te verbeter.
Go nyakišišitšwe ka ga mekgwa ye mentši ya go akanya ka ga molokoloko wa dinako le go dirwa gore e hwetšagale mo mengwageng ye e sa tšwago go feta. Se k e k a le b a k a la g o t šwelela ga mekgwa ya go ithuta ya go diriša metšhene yeo le yona e ilego ya dirišwa ka kakanyong ya molokolokong wa dinako. Go t šwelela ga mehutahuta ya mekgwa le go fapafapana ga yona go tšweletša tlhohlo ge go kgethwa mekgwa ya maleba ya go akanya. Dinyakišišo tše di lekodišišitše go šoma ga mekgwa ye mene ya go akanya yeo e gatetšego pele e lego: ditekanyotshepelo tšeo di kopantšwego tša poelomorago ya maitirišo (ARIMA); dinetweke tša maitirelo tša nyurale (ANN); metšhene ya bekthara ya thekgo (SVM); le mekgwa ya poelomorago yeo e nago le diphošo tša ARIMA. Go kaonafatša go šoma ga yona, nepagalo ya go ithuta ka metšhene le yona e dirišitšwe. Go šoma ga mekgwa ye e fepafapanego go laeditšwe ka go šomiša tshedimošo ya banamedi ba difofane ba Afrika Borwa yeo e kgobokeditšwego mabakeng a dipeakanyo ke Khamphani ya Maemafofane ya Afrika Borwa (ACSA). Sengwalwanyaki šišo se ahlaahlile mekgwa ya kakanyo ye e fapafapanego ka bophara. Dipharologanyi tša go swana le maatla le bofokodi le go dirišega ga mekgwa di ile tša šomišwa. Magato a mangwe ao a tumilego kudu a kakanyo ye e nepagetšego a ile a ahlaahlwa ka nepo ya go kwešiša ka fao a ka šomišwago ka gona ka tshekatshekong ya go šoma ga mekgwa ye. Go hweditšwe gore mokgwa wa poelomorago wa go ba le diphošo tša ARIMA o phadile mekgwa ye mengwe ka moka, gwa latela mokgwa wa ARIMA. Dikutollo tše di sepelelana le dikutollo ka kakaretšo ka dingwaleng. Mo k gwa wa ANN o ka fela o fetišiša gomme se se bonagetše go dipoelo tša tlhahlo le dihlo pha t ša teko ya tshedimošo. Mekgwa ya nepagalo ya go ithuta ka metšhene e bontšhitše dipoelo tšeo di hlakantšwego tšeo di nago le kaonafalo ye kgolo go ye mengwe mekgwa ya go ela go phethagatšwa ga mešomo. Go ka phethwa ka gore mekgwa ya setlwaedi ya go akanya dipalopalo (ARIMA le mokgwa wa poelomorago wa go ba le diphošo tša ARIMA) e šomile bokaone go phala mekgwa ya go ithuta ka metšhene (ANN le SVM) ka mo go sehlopha se sa tshedimošo, go eya ka magato a nepagalo ya magato ao a šomišitšwego. Se se nyaka gore go dirwe dinyakišišo tše dingwe mabapi le go dirišega ga mekgwa ya go ithuta ka metšhene mabapi le go akanya molokoloko wa dinako, e lego seo se tlago thuša go kwešiša le go kaonafatša go šoma ga yona kgahlanong le mekgwa ya setlwaedi ya dipalopalo.
Decision Sciences
M. Sc. (Operations Research)