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Dissertations / Theses on the topic 'Autoregressive neural network'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Trapletti, Adrian, Friedrich Leisch, and Kurt Hornik. "Stationary and integrated autoregressive neural network processes." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/302/1/document.pdf.

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We consider autoregressive neural network (ARNN) processes driven by additive noise. Sufficient conditions on the network weights (parameters) are derived for the ergodicity and stationarity of the process. It is shown that essentially the linear part of the ARNN process determines whether the overall process is stationary. A generalization to the case of integrated ARNN processes is given. Least squares training (estimation) of the stationary models and testing for non-stationarity are discussed. The estimators are shown to be consistent and expressions on the limiting distributions are given

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2

Leisch, Friedrich, Adrian Trapletti, and Kurt Hornik. "On the stationarity of autoregressive neural network models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/1612/1/document.pdf.

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We analyze the asymptotic behavior of autoregressive neural network (AR-NN) processes using techniques from Markov chains and non-linear time series analysis. It is shown that standard AR-NNs without shortcut connections are asymptotically stationary. If linear shortcut connections are allowed, only the shortcut weights determine whether the overall system is stationary, hence standard conditions for linear AR processes can be used.<br>Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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3

Nyman, Nick, and Smura Michel Postigo. "Examining how unforeseen events affect accuracy and recovery of a non-linear autoregressive neural network in stock market prognoses." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186435.

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This report studies how a non-linear autoregressive neural network algorithm for stock market value prognoses is affected by unforeseen events. The study attempts to find out the recovery period for said algorithms after an event, and whether the magnitude of the event affects the recovery period. Tests of 1-day prognoses' deviations from the observed value are carried out on five real stock events and four created simulation sets which exclude the noisy data of the stock market and isolates different kinds of events. The study concludes that the magnitude has no discernible impact on recovery

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4

Forslund, Pontus. "A Neural Network Based Brain-Computer Interface for Classification of Movement Related EEG." Thesis, Linköping University, Department of Mechanical Engineering, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-6481.

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<p>A brain-computer interface, BCI, is a technical system that allows a person to control the external world without relying on muscle activity. This thesis presents an EEG based BCI designed for automatic classification of two dimensional hand movements. The long-term goal of the project is to build an intuitive communication system for operation by people with severe motor impairments. If successful, such system could for example be used by a paralyzed patient to control a word processor or a wheelchair.</p><p>The developed BCI was tested in an offine pilot study. In response to an external

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5

Sarlak, Nermin. "Evaluation And Modeling Of Streamflow Data: Entropy Method, Autoregressive Models With Asymmetric Innovations And Artificial Neural Networks." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606135/index.pdf.

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In the first part of this study, two entropy methods under different distribution assumptions are examined on a network of stream gauging stations located in Kizilirmak Basin to rank the stations according to their level of importance. The stations are ranked by using two different entropy methods under different distributions. Thus, showing the effect of the distribution type on both entropy methods is aimed. In the second part of this study, autoregressive models with asymmetric innovations and an artificial neural network model are introduced. Autoregressive models (AR) which have been d

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ALIEV, KHURSHID. "Internet of Things Applications and Artificial Neural Networks in Smart Agriculture." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2697287.

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Internet of Things (IoT) is receiving a great attention due to its potential strength and ability to be integrated into any complex systems and it is becoming a great tool to acquire data from particular environment to the cloud. Data that are acquired from Wireless Sensor Nodes(WSN) could be predicted using Artificial Neural Network(ANN) models. One of the use case fields of IoT is smart agriculture and there are still issues on developing low cost and power efficient WSN using advanced radio technologies for short and long-range applications and implementation of prediction tools. This is th

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7

Gomes, Leonaldo da Silva. "Redes Neurais Aplicadas Ã InferÃncia dos Sinais de Controle de Dosagem de Coagulantes em uma ETA por FiltraÃÃo RÃpida." Universidade Federal do CearÃ, 2012. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=8105.

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Considerando a importÃncia do controle da coagulaÃÃo quÃmica para o processo de tratamento de Ãgua por filtraÃÃo rÃpida, esta dissertaÃÃo propÃe a aplicaÃÃo de redes neurais artificiais para inferÃncia dos sinais de controle de dosagem de coagulantes principal e auxiliar, no processo de coagulaÃÃo quÃmica em uma estaÃÃo de tratamento de Ãgua por filtraÃÃo rÃpida. Para tanto, foi feito uma anÃlise comparativa da aplicaÃÃo de modelos baseados em redes neurais do tipo: alimentada adiante focada atrasada no tempo (FTLFN); alimentada adiante atrasada no tempo distribuÃda (DTLFN); recorrente de Elma

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8

Alrabady, Linda Antoun Yousef. "An online-integrated condition monitoring and prognostics framework for rotating equipment." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9204.

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Detecting abnormal operating conditions, which will lead to faults developing later, has important economic implications for industries trying to meet their performance and production goals. It is unacceptable to wait for failures that have potential safety, environmental and financial consequences. Moving from a “reactive” strategy to a “proactive” strategy can improve critical equipment reliability and availability while constraining maintenance costs, reducing production deferrals, decreasing the need for spare parts. Once the fault initiates, predicting its progression and deterioration ca

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9

Widing, Härje. "Business analytics tools for data collection and analysis of COVID-19." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176514.

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The pandemic that struck the entire world 2020 caused by the SARS-CoV-2 (COVID-19) virus, will have an enormous interest for statistical and economical analytics for a long time. While the pandemic of 2020 is not the first that struck the entire world, it is the first pandemic in history where the data were gathered to this extent. Most countries have collected and shared its numbers of cases, tests and deaths related to the COVID-19 virus using different storage methods and different data types. Gaining quality data from the COVID-19 pandemic is a problem most countries had during the pandemi

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10

Kalchbrenner, Nal. "Encoder-decoder neural networks." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:d56e48db-008b-4814-bd82-a5d612000de9.

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This thesis introduces the concept of an encoder-decoder neural network and develops architectures for the construction of such networks. Encoder-decoder neural networks are probabilistic conditional generative models of high-dimensional structured items such as natural language utterances and natural images. Encoder-decoder neural networks estimate a probability distribution over structured items belonging to a target set conditioned on structured items belonging to a source set. The distribution over structured items is factorized into a product of tractable conditional distributions over in

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11

Rech, Gianluigi. "Modelling and forecasting economic time series with single hidden-layer feedforward autoregressive artificial neural networks." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-591.

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This dissertation consists of 3 essays In the first essay, A Simple Variable Selection Technique for Nonlinear Models, written in cooperation with Timo Teräsvirta and Rolf Tschernig, I propose a variable selection method based on a polynomial expansion of the unknown regression function and an appropriate model selection criterion. The hypothesis of linearity is tested by a Lagrange multiplier test based on this polynomial expansion. If rejected, a kth order general polynomial is used as a base for estimating all submodels by ordinary least squares. The combination of regressors leading to the

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12

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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13

Hallberg, David, and Erik Renström. "PC Regression, Vector Autoregression, and Recurrent Neural Networks: How do they compare when predicting stock index returns for building efficient portfolios?" Thesis, KTH, Optimeringslära och systemteori, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252557.

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This thesis examines the statistical and economic performance of modeling and predicting equity index returns by application of various statistical models on a set of macroeconomic and financial variables. By combining linear principal component regression, vector autoregressive models, and LSTM neural networks, the authors find that while a majority of the models display high statistical significance, virtually none of them successfully outperform classic portfolio theory on efficient markets in terms of risk-adjusted returns. Several implications are also discussed based on the results.<br>D

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14

Parreira, Fábio José. "Detecção de crises epilépticas a partir de sinais eletroencefalográficos." Universidade Federal de Uberlândia, 2006. https://repositorio.ufu.br/handle/123456789/14290.

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The epilepsy is not a recent phenomenon, even its has being approached and Inves- tigated, this area still demands several researches and it is far away from being totally explained. The obtaining of the primordial features to di®erentiate the epileptic events of the others, in coming signs EEG of scalp, it represents a great challenge, since exist to many artifacts, and these are confused with epileptic events. In this sense, this study presents the development of architectures destined to detect events of epilepsy in coming signs EEG of scalp, capable to aid the professionals of the health i

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15

Claudino, Joana Filipa Caetano. "Intelligent system for time series pattern identification and prediction." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21036.

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Mestrado em Gestão de Sistemas de Informação<br>Os crescentes volumes de dados representam uma fonte de informação potencialmente valiosa para as empresas, mas também implicam desafios nunca antes enfrentados. Apesar da sua complexidade intrínseca, as séries temporais são um tipo de dados notavelmente relevantes para o contexto empresarial, especialmente para tarefas preditivas. Os modelos Autorregressivos Integrados de Médias Móveis (ARIMA), têm sido a abordagem mais popular para tais tarefas, porém, não estão preparados para lidar com as cada vez mais comuns séries temporais de maior dimensã

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16

Абдилдаева, А. А., Олексій Олександрович Дрозденко, Алексей Александрович Дрозденко та ін. "Анализ современных подходов прогнозирования потребления электрической энергии". Thesis, Институт информационных и вычислительных технологий, 2018. http://essuir.sumdu.edu.ua/handle/123456789/68625.

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Проведен обзор современных методов формирования математической модели электроэнергетических систем и разработка интеллектуальной информационной системы мониторинга потребления электроэнергии. Выявлены основные недостатки и преимущества существующих подходов моделирования, а также их применимости для энергетических систем Украины и Казахстана. Определены основные факторы, которые влияют на динамику потребления электроэнергии. Выработаны перечень основных задач, которые необходимо реализовать с целью разработки алгоритмов прогнозирования спроса на электроэнергию для разных объектов, отраслей и у

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17

Sseguya, Raymond. "Forecasting anomalies in time series data from online production environments." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166044.

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Anomaly detection on time series forecasts can be used by many industries in especially forewarning systems that can predict anomalies before they happen. Infor (Sweden) AB is software company that provides Enterprise Resource Planning cloud solutions. Infor is interested in predicting anomalies in their data and that is the motivation for this thesis work. The general idea is firstly to forecast the time series and then secondly detect and classify anomalies on the forecast. The first part is time series forecasting and the second part is anomaly detection and classification done on the forec

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18

Palček, Peter. "Předpovídání vývoje více časových řad při burzovním obchodování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236567.

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The diploma thesis comprises of a general approach used to predict the time series, their categorization, basic characteristics and basic statistical methods for their prediction. Neural networks are also mentioned and their categorization with regards to the suitability for prediction of time series. A program for the prediction of the progress of multiple time series in stock market is designed and implemented, and it's based on a model of flexible neuron tree, whose structure is optimized using immune programming and parameters using a modified version of simulated annealing or particle swa

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19

Liu, Hsin-Ming, and 劉興明. "Combining Autoregressive and Neural Network for Electric Load Forecasting." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/35475046510599534880.

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碩士<br>國立東華大學<br>資訊工程學系<br>92<br>The electric power is one of the important energy that is the foundation for the economic development of a country. For electric power business, accurate load forecasting plays an important role in economic scheduling of generating capacity, scheduling of fuel purchases, planning of energy transactions, and dispatching of generation units. Its effects include the proper schedule and planning of power generation, the reducing of the operation cost, the raising of power supply stability, and the avoidance of electricity restriction or wasting of resources. This th

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20

吳文誌. "A neural network architecture on order determination and parameter estimation of autoregressive moving average model." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/42251193062520150489.

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21

Dietz, Sebastian [Verfasser]. "Autoregressive neural network processes : univariate, multivariate and cointegrated models with application to the German automobile industry / von Sebastian Dietz." 2010. http://d-nb.info/1012842274/34.

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22

Nunes, Diana Catherina Manaig. "Modelling granules size distribution produced on a continuous manufacturating line with non-linear autoregressive artificial neural networks." Master's thesis, 2018. http://hdl.handle.net/10451/40066.

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Tese de mestrado, Engenharia Farmacêutica, Universidade de Lisboa, Faculdade de Farmácia, 2018<br>Particle size is a critical quality parameter in several pharmaceutical unit operations. An adequate particle size distribution is essential to ensure optimal manufacturability which, in turn, has an important impact on the safety, efficacy and quality of the end product. Thus, the monitoring and control of the particle size via in-process size measurements is crucial to the pharmaceutical industry. Currently, a wide range of techniques are available for the determination of particle size distribu

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23

Ngwangwa, Harry Magadhlela. "Road surface profile monitoring based on vehicle response and artificial neural network simulation." Thesis, 2015. http://hdl.handle.net/2263/43788.

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Road damage identification is still largely based on visual inspection methods and profilometer data. Visual inspection methods heavily rely on expert knowledge which is often very subjective. They also result in traffic flow interference due to the need for redirection of traffic to alternative routes during inspection. In addition to this, accurate high-speed profilometers, such as scanning vehicles, are extremely expensive often requiring strong economic justifications for their acquisition. The low-cost profilometers are very slow, typically operating at or less than walking speeds, causin

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24

Cai, Shin-En, and 蔡仕恩. "Applying Run Rule to EWMA Control Charts for Autoregressive Processes with Artificial Neural Networks." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/yu3j8t.

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碩士<br>國立雲林科技大學<br>工業工程與管理系<br>103<br>With technological advancements, SPC has been used in industry to reduce the failure rate. Traditional SPC is good at detecting large changes in process mean but not in small changes. Many studies analyzed EWMA charts, and it was mainly used to detect manufacturing process mean which occurred on small offset change. In past studies, SPC was developed independently and normal distribution. Traditional chats in the autocorrected processes cannot complete the monitoring effectand increase the false alarm rate effectively. In this study, we used the EWMA statis

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25

Lee, Edward, and 李志宏. "A Comparison of Feedbackward Artificial Neural Networks, Autoregressive Integrated Moving Average, Econometric Analysis and Forward Rate Model applied for Exchange Rate Prediction''s Performance." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/81906245362427118718.

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26

Riba, Evans Mogolo. "Exploring advanced forecasting methods with applications in aviation." Diss., 2021. http://hdl.handle.net/10500/27410.

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Abstracts in English, Afrikaans and Northern Sotho<br>More 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

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