Relevant bibliographies by topics / Autoregressive neural network

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Autoregressive neural network'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Autoregressive neural network"

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Blanchard, Tyler, and Biswanath Samanta. "Wind speed forecasting using neural networks." Wind Engineering 44, no. 1 (2019): 33–48. http://dx.doi.org/10.1177/0309524x19849846.

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The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind speed using artificial neural networks. Two variations of artificial neural networks, namely, nonlinear autoregressive neural network and nonlinear autoregressive neural network with exogenous inputs, were used to predict wind speed utilizing 1 year of hourly weather data from four locations around the United States to train, validate, and test these networks. This study optimized both neural network configurations and it demonstrated that both models were suitable for wind speed prediction. Both models outperformed persistence model (with a factor of about 2 to 10 in root mean square error ratio). Both artificial neural network models were implemented for single-step and multi-step-ahead prediction of wind speed for all four locations and results were compared. Nonlinear autoregressive neural network with exogenous inputs model gave better prediction performance than nonlinear autoregressive model and the difference was statistically significant.
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PANDA, CHAKRADHARA, and V. NARASIMHAN. "FORECASTING DAILY FOREIGN EXCHANGE RATE IN INDIA WITH ARTIFICIAL NEURAL NETWORK." Singapore Economic Review 48, no. 02 (2003): 181–99. http://dx.doi.org/10.1142/s0217590803000712.

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This study compares the efficiency of a non-linear model called artificial neural network with linear autoregressive and random walk models in the one-step-ahead prediction of daily Indian rupee/US dollar exchange rate. We find that neural network and linear autoregressive models outperform random walk model in in-sample and out-of-sample forecasts. The in-sample forecasting of neural network is found to be better than that of linear autoregressive model. As far as out-of-sample forecasting is concerned, the results are mixed and we do not find a "winner" model between neural network and linear autoregressive model. However, neural network is able to improve upon the linear autoregressive model in terms of sign predictions. In addition to this, we also find that the number of input nodes has greater impact on neural network's performance than the number of hidden nodes.
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Han, Xu, Huoyue Xiang, Yongle Li, and Yichao Wang. "Predictions of vertical train-bridge response using artificial neural network-based surrogate model." Advances in Structural Engineering 22, no. 12 (2019): 2712–23. http://dx.doi.org/10.1177/1369433219849809.

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To improve the efficiency of reliability calculations for vehicle-bridge systems, we present a surrogate modeling method based on a nonlinear autoregressive with exogenous input artificial neural network model and an important sample, which can forecast responses of dynamic systems, such as vehicle-bridge systems, subjected to stochastic excitations. We also propose a process to analyze the method. A quarter-vehicle model is used to verify the proposed method’s precision, and the nonlinear autoregressive with exogenous input artificial neural network model is used to predict responses of vertical vehicle-bridge systems. The results show that, compared to other training samples, the nonlinear autoregressive with exogenous input artificial neural network model has better prediction accuracy when the sample with the maximum response is considered as an important sample and is used to train the nonlinear autoregressive with exogenous input artificial neural network model, and it requires only two-time numerical simulation (or Monte Carlo simulation) at most, which is used in the training of the nonlinear autoregressive with exogenous input artificial neural network model.
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El Hamidi, Khadija, Mostafa Mjahed, Abdeljalil El Kari, and Hassan Ayad. "Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems." Modelling and Simulation in Engineering 2020 (August 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/8642915.

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In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.
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Gumus, Fatma, and Derya Yiltas-Kaplan. "Congestion Prediction System With Artificial Neural Networks." International Journal of Interdisciplinary Telecommunications and Networking 12, no. 3 (2020): 28–43. http://dx.doi.org/10.4018/ijitn.2020070103.

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Software Defined Network (SDN) is a programmable network architecture that provides innovative solutions to the problems of the traditional networks. Congestion control is still an uncharted territory for this technology. In this work, a congestion prediction scheme has been developed by using neural networks. Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm was performed on the data collected from the OMNET++ simulation. The novelty of this study also covers the implementation of mRMR in an SDN congestion prediction problem. After evaluating the relevance scores, two highest ranking features were used. On the learning stage Nonlinear Autoregressive Exogenous Neural Network (NARX), Nonlinear Autoregressive Neural Network, and Nonlinear Feedforward Neural Network algorithms were executed. These algorithms had not been used before in SDNs according to the best of the authors knowledge. The experiments represented that NARX was the best prediction algorithm. This machine learning approach can be easily integrated to different topologies and application areas.
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Trapletti, Adrian, Friedrich Leisch, and Kurt Hornik. "Stationary and Integrated Autoregressive Neural Network Processes." Neural Computation 12, no. 10 (2000): 2427–50. http://dx.doi.org/10.1162/089976600300015006.

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We consider autoregressive neural network (AR-NN) processes driven by additive noise and demonstrate that the characteristic roots of the shortcuts—the standard conditions from linear time-series analysis—determine the stochastic behavior of the overall AR-NN process. If all the characteristic roots are outside the unit circle, then the process is ergodic and stationary. If at least one characteristic root lies inside the unit circle, then the process is transient. AR-NN processes with characteristic roots lying on the unit circle exhibit either ergodic, random walk, or transient behavior. We also analyze the class of integrated AR-NN (ARI-NN) processes and show that a standardized ARI-NN process “converges” to a Wiener process. Finally, least-squares estimation (training) of the stationary models and testing for nonstationarity is discussed. The estimators are shown to be consistent, and expressions on the limiting distributions are given.
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Valipour, M., M. E. Banihabib, and S. M. R. Behbahani. "Monthly Inflow Forecasting using Autoregressive Artificial Neural Network." Journal of Applied Sciences 12, no. 20 (2012): 2139–47. http://dx.doi.org/10.3923/jas.2012.2139.2147.

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As'ad, Mohamad, Sujito Sujito, and Sigit Setyowibowo. "Neural Network Autoregressive For Predicting Daily Gold Price." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 5, no. 2 (2020): 69–73. http://dx.doi.org/10.25139/inform.v5i2.2715.

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Emas adalah logam mulia yang dapat berfungsi sebagai permata dan juga investasi. Sebagai investasi emas memang praktis karena tidak mudah rusak, mudah diuangkan, tidak kena pajak dan alasan yang lainnya. Sebagai investasi, emas mudah diuangkan ketika dibutuhkan, sehingga banyak masyarakat yang memilih emas sebagai investasi. Supaya berivestasi emas tidak rugi, maka diperlukan perkiraan harga emas saat membeli dan menjual. Banyak metode yang bisa dipakai dalam memprediksi harga emas harian, baik secara statistika maupun secara intelegensi buatan. Pada penelitian ini data yang digunakan adalah data sekunder yang diperoleh dari yahoo finance berupa harga emas harian. Model yang digunakan untuk peramalan yaitu model Neural Network Autoregression (NNAR) dan diperoleh kesimpulan model terbaik adalah NNAR(25,13) yang artinya harga emas harian sekarang dipengaruhi oleh harga emas harian sehari yang lalu sampai dengan 24 periode yang lalu. Model NNAR(25,13) mempunyai akurasi peramalan MASE sebesar 0.5851083, MAPE sebesar 0.370707 dan RMSE sebesar 6.939331.
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As'ad, Mohamad, Sujito Sujito, and Sigit Setyowibowo. "Neural Network Autoregressive For Predicting Daily Gold Price." Jurnal INFORM 5, no. 2 (2020): 69. http://dx.doi.org/10.25139/inform.v0i1.2715.

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Gold is a precious metal that functions as a gem and also an investment. Gold investment is the reason for many people because it is practical, not easily damaged, easy cashed, not taxable, and other purposes. Based on this, many people choose gold as an investment. The problem for people who will invest in gold is related to uncertain gold price predictions so that the accuracy of forecasting methods are needed. The purpose of this paper is to forecast accurately daily gold prices using the Neural Network Autoregressive (NNAR) method. Training Data to find out the value of accuracy in the NNAR method uses secondary data obtained from Yahoo Finance in the form of daily gold prices. Test results on the NNAR method produce a better and more accurate level using the NNAR (25,13) model with a MAPE value of 0.370707, a MASE of 0.5851083, and an RMSE of 6.939331. The conclusion of the results of this paper is the daily price of gold is influenced by the daily price of gold a day ago to 24 periods ago with the NNAR (25,13) model.
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Kim, Jaiyool, and Changryong Baek. "Neural network heterogeneous autoregressive models for realized volatility." Communications for Statistical Applications and Methods 25, no. 6 (2018): 659–71. http://dx.doi.org/10.29220/csam.2018.25.6.659.

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Dissertations / Theses on the topic "Autoregressive neural network"

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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.<br>Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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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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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, and that a sudden event will allow recovery within days regardless of magnitude or change in price development rate. However, less sudden events will cause the recovery period to extend. Noise such as surrounding micro-events, aftershocks, or lingering instability of stock prices will affect accuracy and recovery time significantly.<br>Denna studie undersöker hur ett icke-linjärt autoregressivt neuronnät för aktiemarknadsprognoser påverkas av oväntade händelser. Studien ämnar finna återhämtningsperioden för nätverket efter en händelse, och ta reda på om den initiala påverkan av händelsen påverkar återhämtningen. Tester av endagsprognosers avvikelse från det verkliga värdet genomförs på fem verkliga aktier och fyra skapade dataset som exkluderar den omgivande variationen från aktiemarknaden. Dessa simulerade set isolerar därmed specifika typer av händelser. Studien drar slutsatsen att storleken av händelsen har försumbar betydelse på återhämtningstiden och att plötsliga händelser tillåter återhämtning på några dagar oavsett händelsens ursprungliga storlek eller förändring av prisutvecklingshastighet. Däremot förlänger utdragna händelser återhämtningstiden. Likaså påverkar efterskalv eller kvarvarande instabilitet i prisutvecklingen tillförlitlighet och återhämtningstid avsevärt.
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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 cue, a test subject moved a joystick in one of four directions. During the movement, EEG was recorded from seven electrodes mounted on the subject's scalp. An autoregressive model was fitted to the data, and the extracted coefficients were used as input features to a neural network based classifier. The classifier was trained to recognize the direction of the movements. During the first half of the experiment, real physical movements were performed. In the second half, subjects were instructed just to imagine the hand moving the joystick, but to avoid any muscle activity.</p><p>The results of the experiment indicate that the EEG signals do in fact contain extractable and classifiable information about the performed movements, during both physical and imagined movements.</p>
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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 developed in hydrology are based on several assumptions. The normality assumption for the innovations of AR models is investigated in this study. The main reason of making this assumption in the autoregressive models established is the difficulties faced in finding the model parameters under the distributions other than the normal distributions. From this point of view, introduction of the modified maximum likelihood procedure developed by Tiku et. al. (1996) in estimation of the autoregressive model parameters having non-normally distributed residual series, in the area of hydrology has been aimed. It is also important to consider how the autoregressive model parameters having skewed distributions could be estimated. Besides these autoregressive models, the artificial neural network (ANN) model was also constructed for annual and monthly hydrologic time series due to its advantages such as no statistical distribution and no linearity assumptions. The models considered are applied to annual and monthly streamflow data obtained from five streamflow gauging stations in Kizilirmak Basin. It is shown that AR(1) model with Weibull innovations provides best solutions for annual series and AR(1) model with generalized logistic innovations provides best solution for monthly as compared with the results of artificial neural network models.
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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 the reason why the target of this thesis is to develop a low cost and power efficient WSN and IoT based control system and analyze the best predictive model for such systems. With this purpose, we developed BLESensor node for short-range IoT applications and Internet of Plant(IoP) for long distance smart agriculture applications. A non-linear prediction model is developed in order to forecast acquired data from sensor nodes. BLESensor node Experimental test results reveal that newly developed BLESensor node has a good impact on the improved lifetime and applications could possibly make this emerging technological area more useful. The Android software has been tested on Samsung Galaxy SM-T311, running Android 4.4.2 and it works without any issues and it is supposed to work on all other Android devices equipped with BLE. The working temperature range of the BLESensor node is supposed to work goes from -20 °C to 70 °C due to battery temperature limits. The system has been tested in the climatic chamber (Challenge 250 from Angelantoni) present at the Neuronica Lab, which allowed the sensor to be software calibrated. Several measurements have been proven that each node offers an uncertainty of 1.2 °C for temperature. These values are acceptable for the type of application for which they are intended. The power consumption has been measured directly from scope analysis and simulating the code step by step and calculations resulted that the lifetime of the node lasts for a month. Considering a normal use of these sensors with a reasonable sampling time the lifetime could be increased. IoP node IoP node is a prototype device that works with WiFi protocol and collects temperature, humidity and soil moisture data of plants to the cloud. For IoP node, we have implemented a firmware, tested a prototype device and designed the PCB in OrCAD software and generated a Gerber file and developed an android application. Prediction model Comparisonofthreenon-linearmodelswithOakdatasetresultedinbetterperformance of NNARX model and we used NNARX model to predict 10 days step ahead maximum and minimum temperature and described the results of performances. The performance given by trained models in terms of Mean Square Error (MSE) for maximum temperature prediction provided an error of 0.8826 on unseen data for the month of September. Similarly, the performance of model predicting minimum temperature was tested and it resulted in an error value of 0.944. In conclusion, this work must be intended only as a proof-of-concept, although, the developed BLESensor system, IoP prototype device and predictive models showed expected optimum results, both in terms of functionalities and usability.
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Gomes, Leonaldo da Silva. "Redes Neurais Aplicadas à 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 Elman (ERN) e auto-regressiva nÃo-linear com entradas exÃgenas (NARX). Da anÃlise comparativa, o modelo baseado em redes NARX apresentou melhores resultados, evidenciando o potencial do modelo para uso em casos reais, o que contribuirà para a viabilizaÃÃo de projetos desta natureza em estaÃÃes de tratamento de Ãgua de pequeno porte.<br>Considering the importance of the chemical coagulation control for the water treatment by direct filtration, this work proposes the application of artificial neural networks for inference of dosage control signals of principal and auxiliary coagulant, in the chemical coagulation process in a water treatment plant by direct filtration. To that end, was made a comparative analysis of the application of models based on neural networks, such as: Focused Time Lagged Feedforward Network (FTLFN); Distributed Time Lagged Feedforward Network (DTLFN); Elman Recurrent Network (ERN) and Non-linear Autoregressive with exogenous inputs (NARX). From the comparative analysis, the model based on NARX networks showed better results, demonstrating the potential of the model for use in real cases, which will contribute to the viability of projects of this nature in small size water treatment plants.
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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 can enable timely interventions without risk to personnel safety or to equipment integrity. This work presents an online-integrated condition monitoring and prognostics framework that addresses the above issues holistically. The proposed framework aligns fully with ISO 17359:2011 and derives from the I-P and P-F curve. Depending upon the running state of machine with respect to its I-P and P-F curve an algorithm will do one of the following: (1) Predict the ideal behaviour and any departure from the normal operating envelope using a combination of Evolving Clustering Method (ECM), a normalised fuzzy weighted distance and tracking signal method. (2) Identify the cause of the departure through an automated diagnostics system using a modified version of ECM for classification. (3) Predict the short-term progression of fault using a modified version of the Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS), called here MDENFIS and a tracking signal method. (4) Predict the long term progression of fault (Prognostics) using a combination of Autoregressive Integrated Moving Average (ARIMA)- Empirical Mode Decomposition (EMD) for predicting the future input values and MDENFIS for predicting the long term progression of fault (output). The proposed model was tested and compared against other models in the literature using benchmarks and field data. This work demonstrates four noticeable improvements over previous methods: (1) Enhanced testing prediction accuracy, (2) comparable processing time if not better, (3) the ability to detect sudden changes in the process and finally (4) the ability to identify and isolate the problem source with high accuracy.
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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 pandemic, since it is constantly changing not only for the current situation but also because past values have been altered when additional information has surfaced. The importance of having the latest data available for government officials to make an informed decision, leads to the usage of Business Intelligence tools and techniques for data gathering and aggregation being one way of solving the problem. One of the mostly used software to perform Business Intelligence is the Microsoft develop Power BI, designed to be a powerful visualizing and analysing tool, that could gather all data related to the COVID-19 pandemic into one application. The pandemic caused not only millions of deaths, but it also caused one of the largest drops on the stock market since the Great Recession of 2007. To determine if the deaths or other reasons directly caused the drop, the study modelled the volatility from index funds using Generalized Autoregressive Conditional Heteroscedasticity. One question often asked when talking of the COVID-19 virus, is how deadly the virus is. Analysing the effect the pandemic had on the mortality rate is one way of determining how the pandemic not only affected the mortality rate but also how deadly the virus is. The analysis of the mortality rate was preformed using Seasonal Artificial Neural Network. Forecasting deaths from the pandemic using the Seasonal Artificial Neural Network on the COVID-19 daily deaths data.
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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 individual elements that compose the items. The networks estimate these conditional factors explicitly. We develop encoder-decoder neural networks for core tasks in natural language processing and natural image and video modelling. In Part I, we tackle the problem of sentence modelling and develop deep convolutional encoders to classify sentences; we extend these encoders to models of discourse. In Part II, we go beyond encoders to study the longstanding problem of translating from one human language to another. We lay the foundations of neural machine translation, a novel approach that views the entire translation process as a single encoder-decoder neural network. We propose a beam search procedure to search over the outputs of the decoder to produce a likely translation in the target language. Besides known recurrent decoders, we also propose a decoder architecture based solely on convolutional layers. Since the publication of these new foundations for machine translation in 2013, encoder-decoder translation models have been richly developed and have displaced traditional translation systems both in academic research and in large-scale industrial deployment. In services such as Google Translate these models process in the order of a billion translation queries a day. In Part III, we shift from the linguistic domain to the visual one to study distributions over natural images and videos. We describe two- and three- dimensional recurrent and convolutional decoder architectures and address the longstanding problem of learning a tractable distribution over high-dimensional natural images and videos, where the likely samples from the distribution are visually coherent. The empirical validation of encoder-decoder neural networks as state-of- the-art models of tasks ranging from machine translation to video prediction has a two-fold significance. On the one hand, it validates the notions of assigning probabilities to sentences or images and of learning a distribution over a natural language or a domain of natural images; it shows that a probabilistic principle of compositionality, whereby a high- dimensional item is composed from individual elements at the encoder side and whereby a corresponding item is decomposed into conditional factors over individual elements at the decoder side, is a general method for modelling cognition involving high-dimensional items; and it suggests that the relations between the elements are best learnt in an end-to-end fashion as non-linear functions in distributed space. On the other hand, the empirical success of the networks on the tasks characterizes the underlying cognitive processes themselves: a cognitive process as complex as translating from one language to another that takes a human a few seconds to perform correctly can be accurately modelled via a learnt non-linear deterministic function of distributed vectors in high-dimensional space.
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Books on the topic "Autoregressive neural network"

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Stolzke, Ulf A. NEURONALE NETZE ZUR PROGNOSE VON WARENTERMINPREISEN. Lang AG International Academic Publishers, Peter, 2000.

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Khan, Aman, and Kenneth A. Scorgie. Forecasting Government Budgets. The Rowman & Littlefield Publishing Group, 2022. https://doi.org/10.5040/9781666990355.

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Forecasting is integral to all governmental activities, especially budgetary activities. Without good and accurate forecasts, a government will not only find it difficult to carry out its everyday operations but will also find it difficult to cope with the increasingly complex environment in which it has to operate. This book presents, in a simple and easy to understand manner, some of the commonly used methods in budget forecasting, simple as well as advanced. The book is divided into three parts: It begins with an overview of forecasting background, forecasting process, and forecasting methods, followed by a detailed discussion of the actual methods in Parts I, II, and III. Part I discusses a combination of basic time series models such as percentage average, simple moving average, double moving average, exponential moving average, double as well as triple, simple trend line, time-series with cyclical variation, and time-series regression, with single and multiple independent variables. Part II discusses some of the more advanced, but frequently used time series models, such as ARIMA, regular as well as seasonal, Vector Autoregression (VAR), and Vector Error Correction (VEC). Part III provides an overview of three of the more recent advances in time series models, namely ensemble forecasting, state-space forecasting, and neural network. The book concludes with a brief discussion of some practical issues in budget forecasting.
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Book chapters on the topic "Autoregressive neural network"

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Le, Vuong Minh, Binh Thai Pham, Tien-Thinh Le, Hai-Bang Ly, and Lu Minh Le. "Daily Rainfall Prediction Using Nonlinear Autoregressive Neural Network." In Micro-Electronics and Telecommunication Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2329-8_22.

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Hettiarachchi, Imali T., Asim Bhatti, Paul A. Adlard, and Saeid Nahavandi. "Multivariate Autoregressive-based Neuronal Network Flow Analysis for In-vitro Recorded Bursts." In Neural Information Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26561-2_39.

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Alarcon, Vladimir J. "Predicting Sediment Concentrations Using a Nonlinear Autoregressive Exogenous Neural Network." In Computational Science and Its Applications – ICCSA 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24302-9_42.

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Husin, Wan Zakiyatussariroh Wan, Aina Nafisya Suhaimi, Nur Shuhaila Meor Zambri, Muhammad Azri Aminudin, and Nor Azima Ismail. "Neural Network Autoregressive Model for Forecasting Malaysia Under-5 Mortality." In Data Science and Emerging Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0741-0_32.

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Yigit, Halil, Adnan Kavak, and Metin Ertunc. "Autoregressive and Neural Network Model Based Predictions for Downlink Beamforming." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28648-6_40.

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Le, Tien-Thinh, Binh Thai Pham, Hai-Bang Ly, Ataollah Shirzadi, and Lu Minh Le. "Development of 48-hour Precipitation Forecasting Model using Nonlinear Autoregressive Neural Network." In Lecture Notes in Civil Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0802-8_191.

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Kharola, Ashwani, Rahul Rahul, Vishwjeet Choudhary, and Anurag Bahuguna. "Nonlinear autoregressive neural network based multistep prediction of specific enthalpy of steam." In Recent Advances in Material, Manufacturing, and Machine Learning. CRC Press, 2024. http://dx.doi.org/10.1201/9781003450252-104.

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Laouali, Inoussa Habou, Hamid Qassemi, Manal Marzouq, Antonio Ruano, Saad Bennani Dosse, and Hakim El Fadili. "A Non Linear Autoregressive Neural Network Model for Forecasting Appliance Power Consumption." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6893-4_69.

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Ferreira, J. T., D. Wrbka, and A. van der Merwe. "Some Empirical Findings on Neural Network-Based Forecasting When Subjected to Autoregressive Resampling." In Emerging Topics in Statistics and Biostatistics. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-69622-0_6.

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Kamalov, Firuz, Ikhlaas Gurrib, Sherif Moussa, and Amril Nazir. "A Comparative Study of Autoregressive and Neural Network Models: Forecasting the GARCH Process." In Intelligent Computing Methodologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13832-4_48.

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Conference papers on the topic "Autoregressive neural network"

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Li, Ling. "Digital Labor Education System using Neural Autoregressive Distribution Estimator based Visual Geometry Group-16 Network." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721685.

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Han, Yibo, Ruixuan Ren, Tiejun Li, Jingyi Chen, and Jianmin Zhang. "A-PeARCNN: a Physics-encoded AutoRegressive Convolutional Neural Network with AttentionNet for Solving Partial Differential Equations." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889320.

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Santana, Vinicius V., Carine M. Rebello, Erbet A. Costa, et al. "Recurrent Deep Learning Models for Multi-step Ahead Prediction: Comparison and Evaluation for Real Electrical Submersible Pump (ESP) System." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.107762.

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Predicting processes� future behavior based on past data is vital for automatic control and dynamic optimization in engineering. Recent advances in deep learning, particularly Artificial Neural Networks, have improved predictions in various engineering fields. Recurrent Neural Networks (RNNs) are well-suited for time series data, as they naturally evolve through dynamic systems with recurrent updates. Despite their high predictive power, RNNs may underperform if their training ignores the model's future application. In Model Predictive Control, for example, the model evolves over time using only current information, relying on its own predictions at later steps. A model trained for one-step-ahead predictions may fail when tasked with multi-step-ahead forecasting in autoregressive mode. This study explores deep recurrent neural network models for predicting critical operational time series of a large-scale Electric Submersible Pump system. We present an innovative training approach, framing the task as a multi-step-ahead prediction problem. Results show that aligning model training with its future use is crucial to ensure real-time performance.
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Megalathan, Gajaanuja, and Banuka Athuraliya. "“CrimeLock” A Mobile Application for Analysing and Forecasting Crime using Autoregressive Integrated Moving Average with Artificial Neural Network." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007295.

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Chaudhary, Amit, Saif Obayd Husayn, Rohith Vallabhaneni, Srimaan Yarram, and Shashidhara K S. "Forecasting Retail Sales Demand by using AutoRegressive Integrated Moving Average and Graph Neural Network for Supply Chain Optimization." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070740.

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Minott, David, Masoud Davari, Isaac Otchere, and Frede Blaabjerg. "Replacing Classical Algorithms to Determine the Reliability of Power Electronic Converters: An AI Method Based on the Nonlinear Autoregressive with Exogeneous Inputs Artificial Neural Network." In SoutheastCon 2025. IEEE, 2025. https://doi.org/10.1109/southeastcon56624.2025.10971482.

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Rekik, Siwar, Sid-Ahmed Selouani, Driss Guerchi, and Habib Hamam. "An Autoregressive Time Delay Neural Network for speech steganalysis." In 2012 11th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2012. http://dx.doi.org/10.1109/isspa.2012.6310612.

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Rather, Akhter Mohiuddin. "Optimization of Predicted Portfolio Using Various Autoregressive Neural Networks." In 2012 International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2012. http://dx.doi.org/10.1109/csnt.2012.65.

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Kon, Johan, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, and Tom Oomen. "Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics." In 2022 IEEE 61st Conference on Decision and Control (CDC). IEEE, 2022. http://dx.doi.org/10.1109/cdc51059.2022.9992511.

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Liu Biao, Lu Qing-chun, Jin Zhen-hua, and Nie Sheng-fang. "System identification of locomotive diesel engines with autoregressive neural network." In 2009 4th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2009. http://dx.doi.org/10.1109/iciea.2009.5138836.

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Reports on the topic "Autoregressive neural network"

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Osipov, Gennadij Sergeevich, and Natella Semenovna Vashakidze. Construction of neural network models of the «regression-autoregression» type based on the analytical platform Deductor. Постулат, 2017. http://dx.doi.org/10.18411/postulat-2017-8-8.

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