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Дисертації з теми "Multivariate time series forecasting"

Автор: Grafiati

Опубліковано: 10 травня 2022

Оновлено: 25 липня 2025

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1

Qiang, Fu. "Bayesian multivariate time series models for forecasting European macroeconomic series." Thesis, University of Hull, 2000. http://hydra.hull.ac.uk/resources/hull:8068.

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Research on and debate about 'wise use' of explicitly Bayesian forecasting procedures has been widespread and often heated. This situation has come about partly in response to the dissatisfaction with the poor forecasting performance of conventional methods and partly in view of the development of computational capacity and macro-data availability. Experience with Bayesian econometric forecasting schemes is still rather limited, but it seems to be an attractive alternative to subjectively adjusted statistical models [see, for example, Phillips (1995a), Todd (1984) and West & Harrison (1989)].

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2

Katardjiev, Nikola. "High-variance multivariate time series forecasting using machine learning." Thesis, Uppsala universitet, Institutionen för informatik och media, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-353827.

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There are several tools and models found in machine learning that can be used to forecast a certain time series; however, it is not always clear which model is appropriate for selection, as different models are suited for different types of data, and domain-specific transformations and considerations are usually required. This research aims to examine the issue by modeling four types of machine- and deep learning algorithms - support vector machine, random forest, feed-forward neural network, and a LSTM neural network - on a high-variance, multivariate time series to forecast trend changes one

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3

Lima, Diego Duarte. "A study of demand forecasting cashew trade in CearÃ through multivariate time series." Universidade Federal do CearÃ, 2013. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12185.

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nÃo hÃ<br>The application of time series in varius areas such as engineering, logistics, operations research and economics, aims to provide the knowledge of the dependency between observations, trends, seasonality and forecasts. Considering the lack of effective supporting methods od logistics planning in the area of foreign trade, the multivariate models habe been presented and used in this work, in the area of time series: vector autoregression (VAR), vector autoregression moving-average (VARMA) and state-space integral equation (SS). These models were used for the analysis of demand foreca

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4

Larsson, Klara, and Freja Ling. "Time Series forecasting of the SP Global Clean Energy Index using a Multivariate LSTM." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301904.

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Clean energy and machine learning are subjects that play significant roles in shaping our future. The current climate crisis has forced the world to take action towards more sustainable solutions. Arrangements such as the UN’s Sustainable Development Goals and the Paris Agreement are causing an increased interest in renewable energy solutions. Further, the EU Taxonomy Regulation, applied in 2020, aims to scale up sustainable investments and to direct cash flows toward sustainable projects and activities. These measures create interest in investing in renewable energy alternatives and predictin

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5

Saluja, Rohit. "Interpreting Multivariate Time Series for an Organization Health Platform." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289465.

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Machine learning-based systems are rapidly becoming popular because it has been realized that machines are more efficient and effective than humans at performing certain tasks. Although machine learning algorithms are extremely popular, they are also very literal and undeviating. This has led to a huge research surge in the field of interpretability in machine learning to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process. Moreover, in most real-world problems just making predictions using machine learning algorithms only solves the

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6

Bäärnhielm, Arvid. "Multiple time-series forecasting on mobile network data using an RNN-RBM model." Thesis, Uppsala universitet, Datalogi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-315782.

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The purpose of this project is to evaluate the performance of a forecasting model based on a multivariate dataset consisting of time series of traffic characteristic performance data from a mobile network. The forecasting is made using machine learning with a deep neural network. The first part of the project involves the adaption of the model design to fit the dataset and is followed by a number of simulations where the aim is to tune the parameters of the model to give the best performance. The simulations show that with well tuned parameters, the neural network performes better than the bas

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7

Noureldin, Diaa. "Essays on multivariate volatility and dependence models for financial time series." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:fdf82d35-a5e7-4295-b7bf-c7009cad7b56.

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This thesis investigates the modelling and forecasting of multivariate volatility and dependence in financial time series. The first paper proposes a new model for forecasting changes in the term structure (TS) of interest rates. Using the level, slope and curvature factors of the dynamic Nelson-Siegel model, we build a time-varying copula model for the factor dynamics allowing for departure from the normality assumption typically adopted in TS models. To induce relative immunity to structural breaks, we model and forecast the factor changes and not the factor levels. Using US Treasury yields

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8

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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9

Costantini, Mauro, Cuaresma Jesus Crespo, and Jaroslava Hlouskova. "Can Macroeconomists Get Rich Forecasting Exchange Rates?" WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4181/1/wp176.pdf.

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We provide a systematic comparison of the out-of-sample forecasts based on multivariate macroeconomic models and forecast combinations for the euro against the US dollar, the British pound, the Swiss franc and the Japanese yen. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations help to improve over benchmark trading strategies for the exchange rate against the U

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10

Oscar, Nordström. "Multivariate Short-term Electricity Load Forecasting with Deep Learning and exogenous covariates." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-183982.

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Maintaining the electricity balance between supply and demand is a challenge for electricity suppliers. If there is an under or overproduction, it entails financial costs and affects consumers and the climate. To better understand how to maintain the balance, can the suppliers use short-term forecasts of electricity load. Hence it is of paramount importance that the forecasts are reliable and of high accuracy. Studies show that time series modeling moves towards more data-driven methods, such as Artificial Neural Networks due to their ability to extract complex relationships and flexibility. T

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11

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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Анотація:

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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12

Costantini, Mauro, Cuaresma Jesus Crespo, and Jaroslava Hlouskova. "Forecasting errors, directional accuracy and profitability of currency trading: The case of EUR/USD exchange rate." Wiley, 2016. http://dx.doi.org/10.1002/for.2398.

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Анотація:

We provide a comprehensive study of out-of-sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations, in particular those based on principal components of forecasts, help to improve over benchmark trading strategies, although the excess return per unit of

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13

Backer-Meurke, Henrik, and Marcus Polland. "Predicting Road Rut with a Multi-time-series LSTM Model." Thesis, Högskolan Dalarna, Institutionen för information och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:du-37599.

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Road ruts are depressions or grooves worn into a road. Increases in rut depth are highly undesirable due to the heightened risk of hydroplaning. Accurately predicting increases in road rut depth is important for maintenance planning within the Swedish Transport Administration. At the time of writing this paper, the agency utilizes a linear regression model and is developing a feed-forward neural network for road rut predictions. The aim of the study was to evaluate the possibility of using a Recurrent Neural Network to predict road rut. Through design science research, an artefact in the form

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14

Johansson, David. "Automatic Device Segmentation for Conversion Optimization : A Forecasting Approach to Device Clustering Based on Multivariate Time Series Data from the Food and Beverage Industry." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81476.

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This thesis investigates a forecasting approach to clustering device behavior based on multivariate time series data. Identifying an equitable selection to use in conversion optimization testing is a difficult task. As devices are able to collect larger amounts of data about their behavior it becomes increasingly difficult to utilize manual selection of segments in traditional conversion optimization systems. Forecasting the segments can be done automatically to reduce the time spent on testing while increasing the test accuracy and relevance. The thesis evaluates the results of utilizing mult

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15

Köhler, Steffen [Verfasser], Vasyl [Gutachter] Golosnoy, and Christoph [Gutachter] Hanck. "Modeling, testing and forecasting persistent univariate and multivariate time-series with financial applications / Steffen Köhler ; Gutachter: Vasyl Golosnoy, Christoph Hanck ; Fakultät für Wirtschaftswissenschaft." Bochum : Ruhr-Universität Bochum, 2021. http://d-nb.info/1236813774/34.

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16

Moudiki, Thierry. "Interest rates modeling for insurance : interpolation, extrapolation, and forecasting." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1110/document.

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L'ORSA Own Risk Solvency and Assessment est un ensemble de règles définies par la directive européenne Solvabilité II. Il est destiné à servir d'outil d'aide à la décision et d'analyse stratégique des risques. Dans le contexte de l'ORSA, les compagnies d'assurance doivent évaluer leur solvabilité future, de façon continue et prospective. Pour ce faire, ces dernières doivent notamment obtenir des projections de leur bilan (actif et passif) sur un certain horizon temporel. Dans ce travail de thèse, nous nous focalisons essentiellement sur l'aspect de prédiction des valeurs futures des actifs. Pl

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17

Yongtao, Yu. "Exchange rate forecasting model comparison: A case study in North Europe." Thesis, Uppsala universitet, Statistiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-154948.

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In the past, a lot of studies about the comparison of exchange rate forecasting models have been carried out. Most of these studies have a similar result which is the random walk model has the best forecasting performance. In this thesis, I want to find a model to beat the random walk model in forecasting the exchange rate. In my study, the vector autoregressive model (VAR), restricted vector autoregressive model (RVAR), vector error correction model (VEC), Bayesian vector autoregressive model are employed in the analysis. These multivariable time series models are compared with the random wal

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18

Sävhammar, Simon. "Uniform interval normalization : Data representation of sparse and noisy data sets for machine learning." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19194.

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The uniform interval normalization technique is proposed as an approach to handle sparse data and to handle noise in the data. The technique is evaluated transforming and normalizing the MoodMapper and Safebase data sets, the predictive capabilities are compared by forecasting the data set with aLSTM model. The results are compared to both the commonly used MinMax normalization technique and MinMax normalization with a time2vec layer. It was found the uniform interval normalization performed better on the sparse MoodMapper data set, and the denser Safebase data set. Future works consist of stu

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19

Campos, Celso Vilela Chaves. "Previsão da arrecadação de receitas federais: aplicações de modelos de séries temporais para o estado de São Paulo." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/96/96131/tde-12052009-150243/.

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O objetivo principal do presente trabalho é oferecer métodos alternativos de previsão da arrecadação tributária federal, baseados em metodologias de séries temporais, inclusive com a utilização de variáveis explicativas, que reflitam a influência do cenário macroeconômico na arrecadação tributária, com o intuito de melhorar a acurácia da previsão da arrecadação. Para tanto, foram aplicadas as metodologias de modelos dinâmicos univariados, multivariados, quais sejam, Função de Transferência, Auto-regressão Vetorial (VAR), VAR com correção de erro (VEC), Equações Simultâneas, e de modelos Estrut

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20

Grubb, Howard John. "Multivariate time series modelling." Thesis, University of Bath, 1990. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280803.

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21

Malan, Karien. "Stationary multivariate time series analysis." Pretoria : [s.n.], 2008. http://upetd.up.ac.za/thesis/available/etd-06132008-173800.

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22

Ribeiro, Joana Patrícia Bordonhos. "Outlier identification in multivariate time series." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/22200.

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Анотація:

Mestrado em Matemática e Aplicações<br>Com o desenvolvimento tecnológico, existe uma cada vez maior disponibilidade de dados. Geralmente representativos de situações do dia-a-dia, a existência de grandes quantidades de informação tem o seu interesse quando permite a extração de valor para o mercado. Além disso, surge importância em analisar não só os valores disponíveis mas também a sua associação com o tempo. A existência de valores anormais é inevitável. Geralmente denotados como outliers, a procura por estes valores é realizada comummente com o intuito de fazer a sua exclusão do est

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23

Chen, Chloe Chen. "Graphical modelling of multivariate time series." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/7091.

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This thesis mainly works on the parametric graphical modelling of multivariate time series. The idea of graphical model is that each missing edge in the graph corresponds to a zero partial coherence between a pair of component processes. A vector autoregressive process (VAR) together with its associated partial correlation graph defines a graphical interaction (GI) model. The current estimation methodologies are few and lacking of details when fitting GI models. Given a realization of the VAR process, we seek to determine its graph via the GI model; we proceed by assuming each possible graph a

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24

Aranda, Cotta Higor Henrique. "Robust methods in multivariate time series." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC064.

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Ce manuscrit propose de nouvelles méthodes d’estimation robustes pour les fonctions matricielles d’autocovariance et d’autocorrélation de séries chronologiques multivariées stationnaires pouvant présenter des valeurs aberrantes aléatoires additives. Ces fonctions jouent un rôle important dans l’identification et l’estimation des paramètres de modèles de séries chronologiques multivariées stationnaires. Nous proposons tout d'abord de nouveaux estimateurs des fonctions matricielles d’autocovariance et d’autocorrélation construits en utilisant une approche spectrale à l'aide du périodogramme matr

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25

TOMASI, FEDERICO. "Graphical Models for Multivariate Time-Series." Doctoral thesis, Università degli studi di Genova, 2019. http://hdl.handle.net/11567/941700.

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Анотація:

Gaussian graphical models have received much attention in the last years, due to their flexibility and expression power. In particular, lots of interests have been devoted to graphical models for temporal data, or dynamical graphical models, to understand the relation of variables evolving in time. While powerful in modelling complex systems, such models suffer from computational issues both in terms of convergence rates and memory requirements, and may fail to detect temporal patterns in case the information on the system is partial. This thesis comprises two main contributions in the

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26

Martinho, Carla Alexandra Lopes. "Modelos vectoriais ARMA : estudo e potencialidades." Master's thesis, Instituto Superior de Economia e Gestão, 1997. http://hdl.handle.net/10400.5/21745.

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Mestrado em Matemática Aplicada à Economia e Gestão<br>Neste trabalho vai-se proceder ao estudo e à aplicação prática sobre sucessões cronológicas reais dos modelos vectoriais ARMA. Estes modelos generalizam os modelos univariados ARMA e os modelos multivariados de função transferência, tendo vantagem sobre estes últimos porque permitem a análise conjunta de sucessões cronológicas que apresentam efeito de feedback. E de esperar que a modelação conjunta de sucessões potencie a capacidade de as descrever, obtendo-se ganhos significativos em termos previsionais. Deste modo, procerder-se-á ao estu

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27

Kajitani, Yoshio. "Forecasting time series with neural nets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ39836.pdf.

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28

Policarpi, Andrea. "Transformers architectures for time series forecasting." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25005/.

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Time series forecasting is an important task related to countless applications, spacing from anomaly detection to healthcare problems. The ability to predict future values of a given time series is a nontrivial operation, whose complexity heavily depends on the number and the quality of data available. Historically, the problem has been addressed by statistical models and simple deep learning architectures such as CNNs and RNNs; recently many Transformer-based models have also been used, with excellent results. This thesis work aims to evaluate the performances of two transformer-based model

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29

Lin, Yang. "Deep Learning for Time Series Forecasting." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/29492.

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In this thesis, we develop a collection of deep learning models for time series forecasting. Primary contributions are summarised as three points: Firstly, we explore temporal pattern similarity for time series forecasting and develop three methods: 1) PSF3-MLE, which extends the standard PSF algorithm by using multiple related time series for pattern sequence construction and combines with neural networks as a dynamic meta-learning ensemble; 2) PSNN builds a separate neural network for each pattern sequence type; 3) SpringNet develops Spring DTW attention layers for Transformer. It capture

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30

Dao, Quang Hoan <1992&gt. "Anomaly detection with time series forecasting." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/17320.

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Anomaly detection time series is a very large and complex field. In recent years, several techniques based on data science were designed in order to improve the efficiency of methods developed for this purpose. In this paper, we introduce Recurrent Neural Networks (RNNs) with LSTM units, ARIMA and Facebook Prophet library for dectecting the anomalies with time series forcasting. Due to the challenges in obtaining labeled anomaly datasets, an unsupervised approach is employed. Unsupervised anomaly detection is the process of finding outlying records in a given dataset without prior need for tra

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31

Bodwick, M. K. "Multivariate time series : The search for structure." Thesis, Lancaster University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.233978.

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32

Batres-Estrada, Bilberto. "Deep learning for multivariate financial time series." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168751.

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Deep learning is a framework for training and modelling neural networks which recently have surpassed all conventional methods in many learning tasks, prominently image and voice recognition. This thesis uses deep learning algorithms to forecast financial data. The deep learning framework is used to train a neural network. The deep neural network is a Deep Belief Network (DBN) coupled to a Multilayer Perceptron (MLP). It is used to choose stocks to form portfolios. The portfolios have better returns than the median of the stocks forming the list. The stocks forming the S&P 500 are included

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33

Cheung, Chung-pak, and 張松柏. "Multivariate time series analysis on airport transportation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31976499.

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34

Ghalwash, Mohamed. "Interpretable Early Classification of Multivariate Time Series." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/239730.

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Computer and Information Science<br>Ph.D.<br>Recent advances in technology have led to an explosion in data collection over time rather than in a single snapshot. For example, microarray technology allows us to measure gene expression levels in different conditions over time. Such temporal data grants the opportunity for data miners to develop algorithms to address domain-related problems, e.g. a time series of several different classes can be created, by observing various patient attributes over time and the task is to classify unseen patient based on his temporal observations. In time-sensit

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35

Thu, Huong Nguyen. "Goodness-of-fit in Multivariate Time Series." Doctoral thesis, Universidad Carlos III de Madrid, Department of Statistics, Facultad de Ciencias Sociales y Jurıdicas, Campus de Getafe, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-18770.

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Goodness-of-fit is an important task in time series analysis. In this thesis, wepropose a new family of statistics and a new goodness-of-fit process for the wellknownmultivariate autoregressive moving average VARMA(p,q) model.Some preliminary results are studied first for an initial goodness-of-fit method.Since the residuals of the fit play an important role in identification and diagnosticchecking, relations between least squares residuals and true errors are studied. Anexplicit representation of the information matrix as a limit is also obtained.Second, we generalize a univariate goodness-of

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36

Sánchez, Enríquez Heider Ysaías. "Anomaly detection in streaming multivariate time series." Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/149078.

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Анотація:

Doctor en Ciencias, Mención Computación<br>Este trabajo de tesis presenta soluciones para al problema de detección de anomalı́as en flujo de datos multivariantes. Dado una subsequencia de serie temporal (una pequeña parte de la serie original) como entrada, uno quiere conocer si este corresponde a una observación normal o es una anomalı́a, con respecto a la información histórica. Pueden surgir dificultades debido principalmente a que los tipos de anomalı́a son desconocidos. Además, la detección se convierte en una tarea costosa debido a la gran cantidad de datos y a la existencia de vari

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37

Damle, Chaitanya. "Flood forecasting using time series data mining." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001038.

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38

Pisanelli, Gioele. "Time series forecasting for smart waste management." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22432/.

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Questo lavoro di tesi, svolto in ambito di tirocinio presso Sis.Ter Srl, propone un sistema di ottimizzazione della raccolta dei rifiuti applicato ai cestini della spazzatura presenti sul territorio della città di Delft, in Olanda. Tale sistema crea il percorso migliore utile allo svuotamento dei cestini che più lo necessitano, determinandolo in base allo stato dei cestini attuale (noto grazie a sensori) e futuro (predetto tramite l'uso di reti neurali).

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39

Wohlrabe, Klaus. "Forecasting with mixed-frequency time series models." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-96817.

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40

Fiorucci, José Augusto. "Time series forecasting : advances on Theta method." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/7399.

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Submitted by Caroline Periotto (carol@ufscar.br) on 2016-09-21T14:53:55Z No. of bitstreams: 1 TeseJAF.pdf: 1812104 bytes, checksum: 817ececd9c05df0ddae3a91de3c8bb14 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-23T18:27:05Z (GMT) No. of bitstreams: 1 TeseJAF.pdf: 1812104 bytes, checksum: 817ececd9c05df0ddae3a91de3c8bb14 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-09-23T18:27:11Z (GMT) No. of bitstreams: 1 TeseJAF.pdf: 1812104 bytes, checksum: 817ececd9c05df0ddae3a91de3c8bb14 (MD5)<br>Made available in

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41

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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42

Montagnon, Chris. "Singular value decomposition and time series forecasting." Thesis, Imperial College London, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.535012.

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43

ABELEM, ANTONIO JORGE GOMES. "ARTIFICIAL NEURAL NETWORKS IN TIME SERIES FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1994. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8489@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Esta dissertação investiga a utilização de Redes Neurais Artificiais (RNAs) na previsão de séries temporais, em particular de séries financeiras, consideradas uma classe especial de séries temporais, caracteristicamente ruídos e sem periodicidade aparente. O trabalho envolve quatro partes principais: um estudo sobre redes neurais artificiais e séries temporais; a modelagem das RNAs para previsão de séries temporais; o desenvolvimento de um ambiente de simulação; e o estudo de caso. No estudo sobre Redes Neurais Artificiais

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44

ZANDONADE, ELIANA. "USING NEURAL NETWORK IN TIME SERIES FORECASTING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1993. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8641@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>Este trabalho associa previsão de Séries Temporais a uma nova metodologia de processamento de informação: REDE NEURAL. Usaremos o modelo de Retropropagação, que consiste em uma Rede Neural multicamada com as unidades conectadas apenas com a unidades conectadas apenas com as unidades da camada subseqüente e com a informação passando em uma única direção. Aplicaremos o modelo de retropropagação na análise de quatro séries temporais: uma série ruidosa. Uma série com tendência, uma série sazonal e uma série de Consumo de Ene

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45

Qu, Haizhou. "Financial forecasting using time series and news." Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/22508/.

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This thesis focuses on the field of financial forecasting. Most studies that use the financial news as an input in the prediction process, take it for granted that news has an effect on financial markets. The starting point for this research is the need to question this assumption, and if confirmed, to attempt to quantify it. Therefore, the first study investigates the correlation between news and stock performance based on a dataset covering both trading data and news of 25 companies. We propose a novel framework to quantify the relationship based on two matrices of pairwise distances between

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46

Bhurtel, Bidur Prasad. "CNNs VERSUS LSTMs FOR TIME SERIES FORECASTING." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2830.

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The goal of this thesis is to compare the performances of long short-term memory (LSTM) recurrent neural networks and feedforward convolution neural networks (CNNs) in time series forecasting. The forecasting problem focuses on predicting the future values of a time series using the current and a set of previous (lagged) values of the time series. LSTMs are used extensively in time series forecasting problems because they are specifically designed to process sequential and temporal data. CNNs on the other hand are not designed to process such sequential data. Although CNNs appear to be a p

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47

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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48

Stensholt, B. K. "Statistical analysis of multivariate bilinear time series models." Thesis, University of Manchester, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.582853.

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In the last thirty years there has been extensive research in the analysis of linear time series models. In analyzing univariate and multivariate time series the assumption of linearity is, in many cases, unrealistic. With this in view, recently, many nonlinear models for the analysis of time series have been proposed, mainly for univariate series. One class of models proposed which has received considerable interest, is the class of bilinear models. In particular has the theory of univariate bilinear time series been considered in a number of papers (d. Granger and Andersen (1978), Subba Rao

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49

Marquier, Basile. "Novel Bayesian methods on multivariate cointegrated time series." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/19341/.

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Many economic time series exhibit random walk or trend dynamics and other persistent non-stationary behaviour (e.g. stock prices, exchange rates, unemployment rate and net trading). If a time series is not stationary, then any shock can be permanent and there is no tendency for its level to return to a constant mean over time; moreover, in the long run, the volatility of the process is expected to grow without bound, and the time series cannot be predicted based on historical observations. Cointegration allows the identification of economic integrated time series that exhibit similar dynamics

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Rawizza, Mark Alan. "Time-series analysis of multivariate manufacturing data sets." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10895.

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