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
  6. Reports

Academic literature on the topic 'Autoregressive neural network'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 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
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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 linea
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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 verti
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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 th
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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 hi
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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
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Asad, Muhammad, Usman Qamar, and Muhammad Abbas. "Blood Glucose Level Prediction of Diabetic Type 1 Patients Using Nonlinear Autoregressive Neural Networks." Journal of Healthcare Engineering 2021 (February 26, 2021): 1–7. http://dx.doi.org/10.1155/2021/6611091.

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Diabetes type 1 is a chronic disease which is increasing at an alarming rate throughout the world. Studies reveal that the complications associated with diabetes can be reduced by proper management of the disease by continuously monitoring and forecasting the blood glucose level of patients. Objective. The prior prediction of blood glucose level is necessary to overcome the lag time for insulin absorption in diabetic type 1 patients. Method. In this research, we use continuous glucose monitoring (CGM) data to predict future blood glucose level using the previous data points. We compare two neu
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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 d
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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 NNA
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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
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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
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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
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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 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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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 Elma
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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 ca
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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 pandemi
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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 in
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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 metho
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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 on
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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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