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Journal articles on the topic 'Autoregressive neural network'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

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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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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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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Syed Asad, Hussain, Yuen Richard Kwok Kit, and Lee Eric Wai Ming. "Energy Modeling with Nonlinear-Autoregressive Exogenous Neural Network." E3S Web of Conferences 111 (2019): 03059. http://dx.doi.org/10.1051/e3sconf/201911103059.

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The model-based predictive control (MPC) is considered to be an effective tool for optimal control of building heating, ventilation, and air-conditioning (HVAC) systems. MPC need to update the operating set points of the local control loops that have a significant influence on the energy performance of the system. Performance of MPC relies on the accuracy of the system performance model. There are two commonly used modeling approach – conventional or analytical approach that is the way of process modeling for some time, but it tends to increase the online computational load as it requires a fu

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Taskaya-Temizel, Tugba, and Matthew C. Casey. "A comparative study of autoregressive neural network hybrids." Neural Networks 18, no. 5-6 (2005): 781–89. http://dx.doi.org/10.1016/j.neunet.2005.06.003.

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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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Natsheh and Samara. "Toward Better PV Panel’s Output Power Prediction; a Module Based on Nonlinear Autoregressive Neural Network with Exogenous Inputs." Applied Sciences 9, no. 18 (2019): 3670. http://dx.doi.org/10.3390/app9183670.

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Much work has been carried out for modeling the output power of photovoltaic panels. Using artificial neural networks (ANNS), one could efficiently model the output power of heterogeneous photovoltaic (HPV) panels. However, due to the existing different types of artificial neural network implementations, it has become hard to choose the best approach to use for a specific application. This raises the need for studies that develop models using the different neural networks types and compare the efficiency of these different types for that specific application. In this work, two neural network t

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Jayadianti, Herlina, Tedy Agung Cahyadi, Nur Ali Amri, and Muhammad Fathurrahman Pitayandanu. "METODE KOMPARASI ARTIFICIAL NEURAL NETWORK PADA PREDIKSI CURAH HUJAN - LITERATURE REVIEW." Jurnal Tekno Insentif 14, no. 2 (2020): 48–53. http://dx.doi.org/10.36787/jti.v14i2.150.

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Abstrak - Penelitian untuk mencari model prediksi curah hujan yang akurat di berbagai bidang sudah banyak dilakukan, maka dilakukan di-review kembali guna membantu proses penyaliran dalam perusahaan tambang. Review dilakukan dengan membandingkan hasil dari setiap model yang telah dilakukan pada beberapa penelitian sebelumnya. Penelitian ini menggunakan metode kuantitatif. Model yang dibandingkan pada penelitian di antaranya yaitu model Fuzzy, Fast Fourier Transformation (FFT), Emotional Artificial Neural Network (EANN), Artificial Neural Network (ANN), Adaptive Ensemble Empirical Mode Decompos

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Sani, Muhammad Gaya, Norhaliza Abdul Wahab, Yahya M. Sam, Sharatul Izah Samsudin, and Irma Wani Jamaludin. "Comparison of NARX Neural Network and Classical Modelling Approaches." Applied Mechanics and Materials 554 (June 2014): 360–65. http://dx.doi.org/10.4028/www.scientific.net/amm.554.360.

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Classical optimization tools are effective when precise mechanistic models exist to support their design and implementation. However, most of the real-world processes are complex due to either nonlinearities or uncertainties (or both) and environmental variations, thus making realizing accurate mathematical models for such processes quite difficult or often impossible. Black box approach tends to present a better alternative in such situations. This paper presents a comparison of nonlinear autoregressive with eXogenous (NARX) neural network and traditional modelling techniques [autoregressive

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Patil, Kalpesh, M. C. Deo, Subimal Ghosh, and M. Ravichandran. "Predicting Sea Surface Temperatures in the North Indian Ocean with Nonlinear Autoregressive Neural Networks." International Journal of Oceanography 2013 (April 30, 2013): 1–11. http://dx.doi.org/10.1155/2013/302479.

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Prediction of monthly mean sea surface temperature (SST) values has many applications ranging from climate predictions to planning of coastal activities. Past studies have shown usefulness of neural networks (NNs) for this purpose and also pointed to a need to do more experimentation to improve accuracy and reliability of the results. The present work is directed along these lines. It shows usefulness of the nonlinear autoregressive type of neural network vis-à-vis the traditional feed forward back propagation type. Neural networks were developed to predict monthly SST values based on 61-year

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Ma, Tianhao, Yuchen She, and Junang Liu. "Research on Forest Conversation Analysis Using Autoregressive Neural Network-Based Model." Computational and Mathematical Methods in Medicine 2022 (June 20, 2022): 1–7. http://dx.doi.org/10.1155/2022/3280928.

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Forest biodiversity is an important component of biological diversity that should not be disregarded. The question of how to evaluate it has sparked scholarly inquiry and discussion. The purpose of this paper is to describe the principles of general linear regression, the selection of model variables in OLS autoregressive modelling, model coefficient testing, analysis of variance of autoregressive models, and model evaluation indicators in order to clarify the suitability of GWR models for solving biomass-related data problems. The GWR 4.0 program was used to create a spatially weighted autore

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Sychova, V. V. "COMPARISON OF THE RESULTS OF SHORT-TERM FORECASTING OF ELECTRICITY IMBALANCES OF THE IPS OF UKRAINE USING AUTOREGRESSIVE MODELS AND ARTIFICIAL NEURAL NETWORKS." Praci Institutu elektrodinamiki Nacionalanoi akademii nauk Ukraini 2023, no. 64 (2022): 25–30. http://dx.doi.org/10.15407/publishing2023.64.025.

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The article presents the results of the study of models for short-term forecasting of overall electricity imbalances in the IPS of Ukraine. The analysis of forecasting results obtained using different types of autoregressive models and two forecasting models based on artificial neural networks was performed. Conducted research based on actual data of the balancing market of electric energy of Ukraine showed the effectiveness of using artificial neural networks for the specified task. It is shown that the application of the LSTM (Long short-term memory) artificial neural network model achieves

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Zhan, Junda, Sensen Wu, Jin Qi, et al. "A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation." Geoscientific Model Development 16, no. 10 (2023): 2777–94. http://dx.doi.org/10.5194/gmd-16-2777-2023.

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Abstract. Spatial interpolation, a fundamental spatial analysis method, predicts unsampled spatial data from the values of sampled points. Generally, the core of spatial interpolation is fitting spatial weights via spatial correlation. Traditional methods express spatial distances in a conventional Euclidean way and conduct relatively simple spatial weight calculation processes, limiting their ability to fit complex spatial nonlinear characteristics in multidimensional space. To tackle these problems, we developed a generalized spatial distance neural network (GSDNN) unit to generally and adap

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Wei, Xiaolu, Binbin Lei, Hongbing Ouyang, and Qiufeng Wu. "Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory." Advances in Multimedia 2020 (December 10, 2020): 1–7. http://dx.doi.org/10.1155/2020/8831893.

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This study attempts to predict stock index prices using multivariate time series analysis. The study’s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail. This study applied Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) for prediction to overcome the issue. The results show that stock index prices prediction through the TPA-LSTM algorithm could achieve bett

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Grande, Davide, Catherine A. Harris, Giles Thomas, and Enrico Anderlini. "Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks." Applied Sciences 11, no. 4 (2021): 1829. http://dx.doi.org/10.3390/app11041829.

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Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method

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Hilal, Anwer Mustafa, Mashael M. Asiri, Shaha Al-Otaibi, et al. "Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment." Adsorption Science & Technology 2022 (March 31, 2022): 1–9. http://dx.doi.org/10.1155/2022/6292200.

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Antibiotics become an emerging contaminant and receive more interests due to its ecotoxicological and strong stability in water ecosystems. Antibiotic adsorption onto carbon materials are biochars among the wastewater mechanisms. This research used machine learning (ML) techniques to generate general adsorption forecasting model for sulfamethoxazole (SMX) and tetracycline (TC) on CBM. Dirichlet design parameters and a combined combination of Neumann and Dirichlet boundary situation are applied to the system of differential equations. In addition, the proposed method use the learning under supe

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Awe, Olushina Olawale, and Ronaldo Dias. "Comparative Analysis of ARIMA and Artificial Neural Network Techniques for Forecasting Non-Stationary Agricultural Output Time Series." Agris on-line Papers in Economics and Informatics 14, no. 4 (2022): 3–9. http://dx.doi.org/10.7160/aol.2022.140401.

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With the vast popularity of the deep learning models in the engineering and mathematical fields, Artificial Neural Networks (ANN) have recently attracted significant research applications in agriculture, economics, informatics and finance. In this paper, we use a deep learning method to capture and predict the unknown complex nonlinear characteristics of agricultural output based on autoregressive artificial neural network, using Nigeria as a case study. Using the proposed model, shocks in agricultural output is analyzed and modeled using data obtained for a period of forty years (1980-2019),

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Kaimkhani, Urooj, Bushra Naz, and Sanam Narejo. "Rainfall Prediction Using Time Series Nonlinear Autoregressive Neural Network." International Journal of Computer Science and Engineering 8, no. 1 (2021): 30–38. http://dx.doi.org/10.14445/23488387/ijcse-v8i1p106.

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Sahed, Abdelkader, Mohammed Mekidiche, and Hacen Kahoui. "Forecasting Natural Gas Prices Using Nonlinear Autoregressive Neural Network." International Journal of Mathematical Sciences and Computing 6, no. 5 (2020): 37–46. http://dx.doi.org/10.5815/ijmsc.2020.05.04.

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Barrow, Devon K., and Sven F. Crone. "Cross-validation aggregation for combining autoregressive neural network forecasts." International Journal of Forecasting 32, no. 4 (2016): 1120–37. http://dx.doi.org/10.1016/j.ijforecast.2015.12.011.

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Anand, C. "Comparison of Stock Price Prediction Models using Pre-trained Neural Networks." March 2021 3, no. 2 (2021): 122–34. http://dx.doi.org/10.36548/jucct.2021.2.005.

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Several intelligent data mining approaches, including neural networks, have been widely employed by academics during the last decade. In today's rapidly evolving economy, stock market data prediction and analysis play a significant role. Several non-linear models like neural network, generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) as well as linear models like Auto-Regressive Integrated Moving Average (ARIMA), Moving Average (MA) and Auto Regressive (AR) may be used for stock forecasting. The deep learning architectures

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BOYE, Cynthia Borkai, Peter Ekow BAFFOE, and Paul BOYE. "Shoreline predictive model using artificial intelligence for the homogeneous beach of the Western Coast of Ghana." Nova Geodesia 5, no. 1 (2025): 303. https://doi.org/10.55779/ng51303.

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Coastal areas are preferred home to about 40% of global population than any other ecosystems. Many organisms, including endangered species, live along sandy beaches. Nonetheless, such beaches are prone to erosion and flooding owing to natural and human factors. The shoreline, where land meets the sea, is highly dynamic and challenging to predict with accuracy. Existing predictive numerical models rely on multiple parameters, while statistical analysis of historical shoreline positions assume a linear distribution, overlooking the nonlinear nature of the data. This present study explored the ap

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Sultan Mohd, Mohd Rizman, Juliana Johari, and Fazlina Ahmat Ruslan. "A Systematic Review of Neural Network Autoregressive Model with Exogenous Input for Solar Radiation Prediction Modelling Development." ASM Science Journal 18 (October 30, 2023): 1–12. http://dx.doi.org/10.32802/asmscj.2023.1139.

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Neural Network is one of the Machine Learning methods that has been applied in various Artificial Intelligence system development including solar radiation prediction modelling. Since there are multiple approaches had been developed using the Neural Network method, the study has been focusing on the development of a Multi-layer Neural Network model that can handle non-linearities and highly dynamic data. The integration of the Multi-layer Neural Network and the Non-linear Autoregressive Model with Exogenous Input (NARX) developed a compromising non-linear Neural Network model which can be appl

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Visakha, Manuela, and Dhoriva Urwatul Wutsqa. "PERAMALAN HARGA BERAS MENGGUNAKAN METODE HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN NEURAL NETWORK (ARIMA-NN)." Jurnal Kajian dan Terapan Matematika 9, no. 3 (2023): 148–62. https://doi.org/10.21831/jktm.v9i3.19447.

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Penelitian ini bertujuan untuk mendeskripsikan model dan ketepatan hasil Hybrid ARIMA-NN dalam meramalkan harga beras di Indonesia tahun 2015-2021 yang bersumber dari Badan Pusat Statistik. Hybrid ARIMA-NN adalah model gabungan model Autoregressive Intregated Moving Average (ARIMA) dan Neural Network. Peramalan dilakukan dengan cara melakukan pemodelan ARIMA terlebih dahulu, kemudian residual dari ARIMA dimodelkan dengan Neural Network. Algoritma dalam Neural Network yang digunakan dalam penelitian ini adalah backpropagation. Hasil peramalan menggunakan Hybrid ARIMA-NN diukur keakuratannya men

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Tran Anh, Duong, Thanh Duc Dang, and Song Pham Van. "Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks." J 2, no. 1 (2019): 65–83. http://dx.doi.org/10.3390/j2010006.

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Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station

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Inack, Estelle M., Stewart Morawetz, and Roger G. Melko. "Neural Annealing and Visualization of Autoregressive Neural Networks in the Newman–Moore Model." Condensed Matter 7, no. 2 (2022): 38. http://dx.doi.org/10.3390/condmat7020038.

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Artificial neural networks have been widely adopted as ansatzes to study classical and quantum systems. However, for some notably hard systems, such as those exhibiting glassiness and frustration, they have mainly achieved unsatisfactory results, despite their representational power and entanglement content, thus suggesting a potential conservation of computational complexity in the learning process. We explore this possibility by implementing the neural annealing method with autoregressive neural networks on a model that exhibits glassy and fractal dynamics: the two-dimensional Newman–Moore m

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Hermansah, Hermansah, Dedi Rosadi, Abdurakhman Abdurakhman, and Herni Utami. "SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING." MEDIA STATISTIKA 13, no. 2 (2020): 116–24. http://dx.doi.org/10.14710/medstat.13.2.116-124.

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NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best

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Gopinath, Sumesh, and P. R. Prince. "Prediction of future evolution of solar cycle 24 using machine learning techniques." Proceedings of the International Astronomical Union 13, S340 (2018): 317–18. http://dx.doi.org/10.1017/s1743921318001485.

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AbstractForecasting the solar activity is of great importance not only for its effect on the climate of the Earth but also on the telecommunications, power lines, space missions and satellite safety. In the present work, machine learning using Artificial Neural Networks (ANNs) called Nonlinear Autoregressive Network (NAR) with Exogenous Inputs (NARX) have been applied for the prediction of future evolution of the present sunspot cycle. NARX network is able to combine the performance of ANN algorithm with nonlinear autoregressive method to handle problems such as finding dependencies among sola

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A. Ahmed, Layla. "Comparison of Artificial Neural Network and Box- Jenkins Models to Predict the Number of Patients with Hypertension in Kalar." Ibn AL- Haitham Journal For Pure and Applied Sciences 33, no. 4 (2020): 110. http://dx.doi.org/10.30526/33.4.2516.

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Artificial Neural Network (ANN) is widely used in many complex applications. Artificial neural network is a statistical intelligent technique resembling the characteristic of the human neural network. The prediction of time series from the important topics in statistical sciences to assist administrations in the planning and make the accurate decisions, so the aim of this study is to analysis the monthly hypertension in Kalar for the period (January 2011- June 2018) by applying an autoregressive –integrated- moving average model and artificial neural networks and choose the best and most effic

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Khrisat, Mohammad S., Anwar Alabadi, Saleh Khawatreh, Majed Omar Al-Dwairi, and Ziad A. Alqadi. "Autoregressive prediction analysis using machine deep learning." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1509. http://dx.doi.org/10.11591/ijeecs.v27.i3.pp1509-1516.

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Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear aut

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Khrisat, Mohammad S., Anwar Alabadi, Saleh Khawatreh, Majed Omar Al-Dwairi, and Ziad A. Alqadi. "Autoregressive prediction analysis using machine deep learning." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 3 (2022): 1509–15. https://doi.org/10.11591/ijeecs.v27.i3.pp1509-1515.

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Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear aut

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Niranjana Murthy, H. S. "Comparison Between Non-Linear Autoregressive and Non-Linear Autoregressive with Exogeneous Inputs Models for Predicting Cardiac Ischemic Beats." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 3974–78. http://dx.doi.org/10.1166/jctn.2020.9001.

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The prediction accuracy and generalization ability of neural models for forecasting Myocardial Ischemic Beats depends on type and architecture of employed network model. This paper presents the comparison analysis of recurrent neural network (RNN) architectures with embedded memory, Non-linear Autoregressive (NAR) and Non-linear Autoregressive with Exogeneous inputs (NARX) models for forecasting Ischemic Beats in ECG. Numerous architectures of the NAR and NARX models are verified for prediction and the performances are evaluated in terms of MSE. The performances of NAR and NARX models are vali

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J., C. Ramesh Reddy, Ganesh T., Venkateswaran M., and R. S. Reddy P. "RAINFALL FORECASTOF ANDHRA PRADESH USING ARTIFICIAL NEURAL NETWORKS." International Journal of Current Research and Modern Education 2, no. 2 (2017): 223–34. https://doi.org/10.5281/zenodo.1045247.

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Forecasting monthly mean rainfall of Andhra Pradesh (India)using seasonal autoregressive integrated moving average (SARIMA) modeland artificial neural networks (ANN)has been discussed.In this paper, we have given the prediction values according to SARIMA and neural network models, in whichwe found that the ARIMA (1,0,0)(2,0,0)[12] for actual dataand ARIMA (3,0,0)(2,0,0)[12] for rainfall differenceshas been fitted.The significance test has been made by using lowest AIC and BIC values.

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Al Bitar, N., and A. I. Gavrilov. "Using Artificial Neural Networks to Compensate for the Error in an Integrated Navigation System." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 2 (131) (June 2020): 4–26. http://dx.doi.org/10.18698/0236-3933-2020-2-4-26.

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The paper presents a new method for improving the accuracy of an integrated navigation system in terms of coordinate and velocity when there is no signal received from the global navigation satellite system. We used artificial neural networks to simulate the error occurring in an integrated navigation system in the absence of the satellite navigation system signal. We propose a method for selecting the inputs for the artificial neural networks based on the mutual information (MI) criterion and lag-space estimation. The artificial neural network employed is a non-linear autoregressive neural ne

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Skulovich, Olya, Caroline Ganal, Leonie K. Nüßer, et al. "Prediction of erosional rates for cohesive sediments in annular flume experiments using artificial neural networks." H2Open Journal 1, no. 2 (2018): 99–111. http://dx.doi.org/10.2166/h2oj.2018.107.

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Abstract Artificial neural network is used to predict development of suspended sediment concentration in annular flume experiments on cohesive sediment erosion. Natural sediment for the experiments was taken from the River Rhine and subjected to a consecutive increase in the bed shear stress. The development of the suspended particulate matter (SPM) was measured and then utilized for artificial neural network training, validation, and testing, including independent testing on new data sets. Several network configurations were examined, in particular, with and without autoregressive input. Addi

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Mohammed, Ahmed Salahuddin, Amin Salih Mohammed, and Shahab Wahhab Kareem. "Deep Learning and Neural Network-Based Wind Speed Prediction Model." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 03 (2022): 403–25. http://dx.doi.org/10.1142/s021848852240013x.

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This paper aims to develop a wind speed prediction model by utilizing deep learning and neural networks. The analysis of weather data using a neural network architecture has been completed. The Long Short-Term Memory (LSTM) architecture is a type of artificial Recurrent Neural Network (RNN) used in deep learning is the first method plots the predicting Wind Speed based on the dataset and predicts the future spread. A dataset from a real-time weather station is used in the implementation model. The dataset consists of information from the weather station implements of the recurrent neural netwo

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Sandeep, Yadav. "Predictive Modeling of Cryptocurrency Price Movements Using Autoregressive and Neural Network Models." International Journal on Science and Technology 14, no. 1 (2023): 1–9. https://doi.org/10.5281/zenodo.14288541.

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Cryptocurrency markets are highly volatile and driven by complex, non-linear dynamics, posing significant challenges for price prediction. This research explores the predictive modeling of cryptocurrency price movements by integrating traditional statistical techniques, such as Autoregressive (AR) models, with advanced Neural Network (NN) architectures. The study evaluates the performance of these models in forecasting short-term price trends for major cryptocurrencies like Bitcoin, Ethereum, and Binance Coin. The dataset consists of historical price data and technical indicators, preprocessed

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Benrhmach, Ghassane, Khalil Namir, Jamal Bouyaghroumni, and Abdelwahed Namir. "Nonlinear Autoregressive Neural Network and Wavelet Transform for Rainfall Prediction." Mathematical Models and Computer Simulations 14, no. 5 (2022): 837–46. http://dx.doi.org/10.1134/s2070048222050027.

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Lutoslawski, Krzysztof, Marcin Hernes, Joanna Radomska, Monika Hajdas, Ewa Walaszczyk, and Agata Kozina. "Food Demand Prediction Using the Nonlinear Autoregressive Exogenous Neural Network." IEEE Access 9 (2021): 146123–36. http://dx.doi.org/10.1109/access.2021.3123255.

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Kravtsov, G. О., А. N. Prymushko, and V. І. Koshell. "Koshell Combined Autoregressive-Neural Network Method for Predicting Time Series." Èlektronnoe modelirovanie 42, no. 4 (2020): 3–14. http://dx.doi.org/10.15407/emodel.42.04.003.

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Yasin, Hasbi, Budi Warsito, Rukun Santoso, and Suparti. "Soft Computation Vector Autoregressive Neural Network (VAR-NN) GUI-Based." E3S Web of Conferences 73 (2018): 13008. http://dx.doi.org/10.1051/e3sconf/20187313008.

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Vector autoregressive model proposed for multivariate time series data. Neural Network, including Feed Forward Neural Network (FFNN), is the powerful tool for the nonlinear model. In autoregressive model, the input layer is the past values of the same series up to certain lag and the output layers is the current value. So, VAR-NN is proposed to predict the multivariate time series data using nonlinear approach. The optimal lag time in VAR are used as aid of selecting the input in VAR-NN. In this study we develop the soft computation tools of VAR-NN based on Graphical User Interface. In each nu

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Lu, Si Wei, and He Xu. "Textured image segmentation using autoregressive model and artificial neural network." Pattern Recognition 28, no. 12 (1995): 1807–17. http://dx.doi.org/10.1016/0031-3203(95)00051-8.

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Han, Hyungseob, Dajung Kim, and Uipil Chong. "Automatic drowsiness detection system using autoregressive coefficients and neural network." Journal of the Acoustical Society of America 131, no. 4 (2012): 3250. http://dx.doi.org/10.1121/1.4708128.

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