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Dissertations / Theses on the topic 'Time-series analysis. Forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Yiu, Fu-keung. "Time series analysis of financial index /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18003047.

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2

Mok, Ching-wah. "A comparison of two approaches to time series forecasting." Hong Kong : University of Hong Kong, 1993. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20666342.

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3

Yiu, Fu-keung, and 饒富強. "Time series analysis of financial index." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267804.

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4

Kibar, Mustafa Alptekin. "Building Cost Index Forecasting With Time Series Analysis." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608686/index.pdf.

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Building cost indices are widely used in construction industry to measure the rate of change of building costs as a combination of labor and material costs. Cost index forecast is crucial for the two main parties of construction industry, contactor, and the client. Forecast information is used to increase the accuracy of estimate for the project cost to evaluate the bid price. The aim of this study is to develop time series models to forecast building cost indices in Turkey and United States. The models developed are compared with regression analysis and simple averaging models in terms of p

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5

Billah, Baki 1965. "Model selection for time series forecasting models." Monash University, Dept. of Econometrics and Business Statistics, 2001. http://arrow.monash.edu.au/hdl/1959.1/8840.

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6

Al-Madfai, Hasan. "Weather corrected electricity demand forecasting." Thesis, University of South Wales, 2002. https://pure.southwales.ac.uk/en/studentthesis/weather-corrected-electricity-demand-forecasting(2e066cc4-58b1-4694-9937-ee8f57fbed02).html.

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Electricity load forecasts now form an essential part of the routine operations of electricity companies. The complexity of the short-term load forecasting (STLF) problem arises from the multiple seasonal components, the change in consumer behaviour during holiday seasons and other social and religious events that affect electricity consumption. The aim of this research is to produce models for electricity demand that can be used to further the understanding of the dynamics of electricity consumption in South Wales. These models can also be used to produce weather corrected forecasts, and to p

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7

莫正華 and Ching-wah Mok. "A comparison of two approaches to time series forecasting." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1993. http://hub.hku.hk/bib/B31977431.

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8

Kwok, Sai-man Simon. "Statistical inference of some financial time series models." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36885654.

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9

Ng, C. N. "Recursive identification, estimation and forecasting of non-stationary time series." Thesis, Lancaster University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383583.

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10

Barbosa, Emanuel Pimentel. "Dynamic Bayesian models for vector time series analysis & forecasting." Thesis, University of Warwick, 1989. http://wrap.warwick.ac.uk/34817/.

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This thesis considers the Bayesian analysis of general multivariate DLM's (Dynamic Linear Models) for vector time series forecasting where the observational variance matrices are unknown. This extends considerably some previous work based on conjugate analysis for a special sub—class of vector DLM's where all marginal univariate models follow the same structure. The new methods developed in this thesis, are shown to have a better performance than other competing approaches to vector DLM analysis, as for instance, the one based on the Student t filter. Practical aspects of implementation of the

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11

De, Klerk Jacques. "Time series forecasting and model selection in singular spectrum analysis." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/53190.

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Dissertation (PhD)--University of Stellenbosch, 2002<br>ENGLISH ABSTRACT: Singular spectrum analysis (SSA) originated in the field of Physics. The technique is non-parametric by nature and inter alia finds application in atmospheric sciences, signal processing and recently in financial markets. The technique can handle a very broad class of time series that can contain combinations of complex periodicities, polynomial or exponential trend. Forecasting techniques are reviewed in this study, and a new coordinate free joint-horizon k-period-ahead forecasting formulation is derived. The stu

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12

Barbosa, Emanuel Pimentel. "Dynamic Baysian models for vector time series analysis and forecasting." Online version, 1989. http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.254394.

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13

Lee, Fung-Man. "Studies in time series analysis and forecasting of energy data." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36032.

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14

Wallentinsson, Emma Wallentinsson. "Multiple Time Series Forecasting of Cellular Network Traffic." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154868.

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The mobile traffic in cellular networks is increasing in a steady rate as we go intoa future where we are connected to the internet practically all the time in one wayor another. To map the mobile traffic and the volume pressure on the base stationduring different time periods, it is useful to have the ability to predict the trafficvolumes within cellular networks. The data in this work consists of 4G cellular trafficdata spanning over a 7 day coherent period, collected from cells in a moderately largecity. The proposed method in this work is ARIMA modeling, in both original formand with an ex

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15

Edwards, Samuel Zachary. "Forecasting Highly-Aggregate Internet Time Series Using Wavelet Techniques." Thesis, Virginia Tech, 2006. http://hdl.handle.net/10919/33223.

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The U.S. Coast Guard maintains a network structure to connect its nation-wide assets. This paper analyzes and models four highly aggregate traces of the traffic to/from the Coast Guard Data Network ship-shore nodes, so that the models may be used to predict future system demand. These internet traces (polled at 5â 40â intervals) are shown to adhere to a Gaussian distribution upon detrending, which imposes limits to the exponential distribution of higher time-resolution traces. Wavelet estimation of the Hurst-parameter is shown to outperform estimation by another common method (Sample-Var

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16

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.

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This dissertation comprises of three original contributions to empirical forecasting research. Chapter 1 introduces the dissertation. Chapter 2 contributes to the literature on hierarchical time series (HTS) modelling by proposing a disaggregated forecasting system for both inflation rate and its volatility. Using monthly data that underlies the Retail Prices Index for the UK, we analyse the dynamics of the inflation process. We examine patterns in the time-varying covariation among product-level inflation rates that aggregate up to industry-level inflation rates that in turn aggregate up to t

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17

Conway, Eunan Martin. "Stochastic modelling and forecasting of solar radiation." Thesis, Northumbria University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367414.

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18

Tsang, Fan Cheong. "Advances in flood forecasting using radar rainfalls and time-series analysis." Thesis, Lancaster University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.481184.

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This thesis reports the use of a time-series analysis approach to study the catchment hydrological system of the River Ribble. Rain gauge records, radar rainfall estimates and flow data are used in the analysis. The preliminary study consists of the flow forecasting at Reedyford, Pendle Water (82 km2). Flow forecasts generated from the rain gauge records are better than the radar rainfall estimates over this small catchment. However, the catchment response to rainfall is quick and no clear advantages in extending the lead-time of the forecast can be introduced by using an artificial time delay

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19

Fagerholm, Christian. "Time series analysis and forecasting : Application to the Swedish Power Grid." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-88615.

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n the electrical power grid, the power load is not constant but continuouslychanging. This depends on many different factors, among which the habits of theconsumers, the yearly seasons and the hour of the day. The continuous change inenergy consumption requires the power grid to be flexible. If the energy provided bygenerators is lower than the demand, this is usually compensated by using renewablepower sources or stored energy until the power generators have adapted to the newdemand. However, if buffers are depleted the output may not meet the demandedpower and could cause power outages. The

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20

Coroneo, Laura. "Essays on modelling and forecasting financial time series." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210284.

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This thesis is composed of three chapters which propose some novel approaches to model and forecast financial time series. The first chapter focuses on high frequency financial returns and proposes a quantile regression approach to model their intraday seasonality and dynamics. The second chapter deals with the problem of forecasting the yield curve including large datasets of macroeconomics information. While the last chapter addresses the issue of modelling the term structure of interest rates. <p><p>The first chapter investigates the distribution of high frequency financial returns, with sp

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21

Åkerlund, Agnes. "Time-Series Analysis of Pulp Prices." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39726.

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The pulp and paper industry has a significant role in Europe’s economy and society, and its significance is still growing. The pulp market and the customers’ requirements are highly affected by the pulp market prices and the requested kind of pulp, i.e., Elementary Chlorine Free (ECF) or Total Chlorine Free (TCF). There is a need to predict different market aspects, where the market price is one, to gain a better understanding of a business situation. Understanding market dynamics can support organizations to optimize their processes and production. Forecasting future pulp prices has not recen

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22

Arora, Siddharth. "Time series forecasting with applications in macroeconomics and energy." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:c763b735-e4fa-4466-9c1f-c3f6daf04a67.

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The aim of this study is to develop novel forecasting methodologies. The applications of our proposed models lie in two different areas: macroeconomics and energy. Though we consider two very different applications, the common underlying theme of this thesis is to develop novel methodologies that are not only accurate, but are also parsimonious. For macroeconomic time series, we focus on generating forecasts for the US Gross National Product (GNP). The contribution of our study on macroeconomic forecasting lies in proposing a novel nonlinear and nonparametric method, called weighted random ana

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23

梁桂鏈 and Kwai-lin Leung. "An experiment with turning point forecasts using Hong Kong time seriesdata." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1989. http://hub.hku.hk/bib/B31975975.

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24

Kwok, Sai-man Simon, and 郭世民. "Statistical inference of some financial time series models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36885654.

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25

Norvell, Joakim. "Statistical forecasting and product portfolio management." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-116866.

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For a company to stay profitable and be competitive, the customer satisfaction must be very high. This means that the company must provide the right item at the right place at the right time, or the customer may bring its business to the competitor. But these factors bring uncertainty for the company in the supply chain of when, what and how much of the item to produce and distribute. For reducing this uncertainty and for making better plans for future demand, some sort of forecasting method must be provided. A forecast can however be statistically based and also completed with a judgmental kn

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26

Li, Tak-wai Wilson. "Forecasting of tide heights : an application of smoothness priors in time series modelling /." [Hong Kong] : University of Hong Kong, 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13154357.

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27

Zhao, Ping. "Time series analysis and forecasting with the application of SAS in forecasting tourist arrivals in Macau." Thesis, University of Macau, 2004. http://umaclib3.umac.mo/record=b1447314.

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28

Lu, Zhen Cang. "Price forecasting models in online flower shop implementation." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691395.

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29

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 t

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30

MEDEIROS, MARCELO CUNHA. "A LINEAR-NEURAL HYBRID MODEL FOR ANALYSIS AND FORECASTING OF TIME-SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1998. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14540@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Esta dissertação apresenta um modelo não linear auto-regressivo com variáveis exógenas (ARX), para análise e previsão de séries temporais. Os coeficientes do modelo são estimados pela saída de uma rede neural feed-forward, treinada por um algoritmo híbrido de otimização. Os resultados obtidos são comparados tanto com modelos lineares, quanto com não lineares.<br>This thesis presents a non linear autoregressive model with exogeneous variables (ARX), for time series analysis and forecasting. The coefficients of the model are given

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31

Winn, David. "An analysis of neural networks and time series techniques for demand forecasting." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1004362.

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This research examines the plausibility of developing demand forecasting techniques which are consistently and accurately able to predict demand. Time Series Techniques and Artificial Neural Networks are both investigated. Deodorant sales in South Africa are specifically studied in this thesis. Marketing techniques which are used to influence consumer buyer behaviour are considered, and these factors are integrated into the forecasting models wherever possible. The results of this research suggest that Artificial Neural Networks can be developed which consistently outperform industry forecasti

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32

Ribeiro, Ramos Francisco Fernando, and fr1960@clix pt. "Essays in time series econometrics and forecasting with applications in marketing." RMIT University. Economics, Finance and Marketing, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20071220.144516.

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This dissertation is composed of two parts, an integrative essay and a set of published papers. The essay and the collection of papers are placed in the context of development and application of time series econometric models in a temporal-axis from 1970s through 2005, with particular focus in the Marketing discipline. The main aim of the integrative essay is on modelling the effects of marketing actions on performance variables, such as sales and market share in competitive markets. Such research required the estimation of two kinds of time series econometric models: multivariate and multip

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33

Setyawati, Bina R. "Multi-layer feed forward neural networks for foreign exchange time series forecasting." Morgantown, W. Va. : [West Virginia University Libraries], 2005. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=4180.

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Thesis (Ph. D.)--West Virginia University, 2005.<br>Title from document title page. Document formatted into pages; contains xii, 185 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 140-146).

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34

Flores, João Henrique Ferreira. "ARMA-CIGMN : an Incremental Gaussian Mixture Network for time series analysis and forecasting." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/116126.

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Este trabalho apresenta um novo modelo de redes neurais para análise e previsão de séries temporais: o modelo ARMA-CIGMN (do inglês, Autoregressive Moving Average Classical Incremental Gaussian Mixture Network) além dos resultados obtidos pelo mesmo. Este modelo se baseia em modificações realizadas em uma versão reformulada da IGMN. A IGMN Clássica, CIGMN, é similar à versão original da IGMN, porém baseada em uma abordagem estatística clássica, a qual também é apresentada neste trabalho. As modificações do algoritmo da IGMN foram feitas para melhor adpatação a séries temporais. O modelo ARMA-C

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35

Ben, Taieb Souhaib. "Machine learning strategies for multi-step-ahead time series forecasting." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209234.

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How much electricity is going to be consumed for the next 24 hours? What will be the temperature for the next three days? What will be the number of sales of a certain product for the next few months? Answering these questions often requires forecasting several future observations from a given sequence of historical observations, called a time series. <p><p>Historically, time series forecasting has been mainly studied in econometrics and statistics. In the last two decades, machine learning, a field that is concerned with the development of algorithms that can automatically learn from data, ha

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36

Li, Tak-wai Wilson, and 李德煒. "Forecasting of tide heights: an application of smoothness priors in time series modelling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B3121048X.

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37

Novak, Martina. "A neural network approach for simulation and forecasting of chaotic time series." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/19087.

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38

Stockhammar, Pär. "Some Contributions to Filtering, Modeling and Forecasting of Heteroscedastic Time Series." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-38627.

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Heteroscedasticity (or time-dependent volatility) in economic and financial time series has been recognized for decades. Still, heteroscedasticity is surprisingly often neglected by practitioners and researchers. This may lead to inefficient procedures. Much of the work in this thesis is about finding more effective ways to deal with heteroscedasticity in economic and financial data. Paper I suggest a filter that, unlike the Box-Cox transformation, does not assume that the heteroscedasticity is a power of the expected level of the series. This is achieved by dividing the time series by a movin

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39

Plouffe, Jeffrey Stewart. "The development and validation of a fuzzy logic method for time-series extrapolation /." View online ; access limited to URI, 2005. http://0-wwwlib.umi.com.helin.uri.edu/dissertations/dlnow/3186918.

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40

Manero, Font Jaume. "Deep learning architectures applied to wind time series multi-step forecasting." Doctoral thesis, Universitat Politècnica de Catalunya, 2020. http://hdl.handle.net/10803/669283.

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Forecasting is a critical task for the integration of wind-generated energy into electricity grids. Numerical weather models applied to wind prediction, work with grid sizes too large to reproduce all the local features that influence wind, thus making the use of time series with past observations a necessary tool for wind forecasting. This research work is about the application of deep neural networks to multi-step forecasting using multivariate time series as an input, to forecast wind speed at 12 hours ahead. Wind time series are sequences of meteorological observations like wind speed, te

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41

Chan, Eric Glenn. "Forecasting the S&P 500 index using time series analysis and simulation methods." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55206.

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Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, System Design and Management Program, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 100-102).<br>The S&P 500 represents a diverse pool of securities in addition to Large Caps. A range of audiences are interested in the S&P 500 forecasts including investors, speculators, economists, government and researchers. The primary objective is to attempt to provide an accurate 3 month and 12 month forecast using the recent credit crisis data, specifically during the time range

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42

Ngan, Wai Seng. "The application of Box-Jenkins models to the forecast of time series of Mainland China tourists in Macao." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493241.

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43

Koller, Simon. "Multiple Time Series Analysis of Freight Rate Indices." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288500.

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In this master thesis multiple time series of shipping industry and financial data are analysed in order to create a forecasting model to forecast freight rate indices. The data of main interest which are predicted are the two freight rate indices, BDI and BDTI, from the Baltic Exchange. The project investigates the possibilities for aggregated Vector Autoregression(VAR) models to outperform simple univariate models, in this case, an Autoregressive Integrated Moving Average(ARIMA) with seasonal components. The other part of this thesis is to model market shocks in the freight rate indices, giv

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44

Xie, Yingfu. "Maximum likelihood estimation and forecasting for GARCH, Markov switching, and locally stationary wavelet processes /." Umeå : Dept. of Forest Economics, Swedish University of Agricultural Sciences, 2007. http://epsilon.slu.se/2007107.pdf.

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Cheng, Xixin. "Mixture time series models and their applications in volatility estimation and statistical arbitrage trading." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B40988053.

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46

Schwartz, Michael. "Optimized Forecasting of Dominant U.S. Stock Market Equities Using Univariate and Multivariate Time Series Analysis Methods." Chapman University Digital Commons, 2017. http://digitalcommons.chapman.edu/comp_science_theses/3.

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This dissertation documents an investigation into forecasting U.S. stock market equities via two very different time series analysis techniques: 1) autoregressive integrated moving average (ARIMA), and 2) singular spectrum analysis (SSA). Approximately 40% of the S&P 500 stocks are analyzed. Forecasts are generated for one and five days ahead using daily closing prices. Univariate and multivariate structures are applied and results are compared. One objective is to explore the hypothesis that a multivariate model produces superior performance over a univariate configuration. Another objective

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47

Kalmár, Marcus, and Joel Nilsson. "The art of forecasting – an analysis of predictive precision of machine learning models." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-280675.

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Forecasting is used for decision making and unreliable predictions can instill a false sense of condence. Traditional time series modelling is astatistical art form rather than a science and errors can occur due to lim-itations of human judgment. In minimizing the risk of falsely specifyinga process the practitioner can make use of machine learning models. Inan eort to nd out if there's a benet in using models that require lesshuman judgment, the machine learning models Random Forest and Neural Network have been used to model a VAR(1) time series. In addition,the classical time series models A

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Smith, Aaron D. "Stochastic permanent breaks /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1999. http://wwwlib.umi.com/cr/ucsd/fullcit?p9938588.

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49

Qi, Jing. "Application of Intervention Analysis to Evaluate the Impacts of Special Events on Freeways." FIU Digital Commons, 2008. http://digitalcommons.fiu.edu/etd/71.

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In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near

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50

Zhao, Tao. "A new method for detection and classification of out-of-control signals in autocorrelated multivariate processes." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5615.

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Thesis (M.S.)--West Virginia University, 2008.<br>Title from document title page. Document formatted into pages; contains x, 111 p. : ill. Includes abstract. Includes bibliographical references (p. 102-106).

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