Nonlinear autoregressive neural network and extended Kalman filters for prediction of financial time series
type: Item
instance of: scholarly article
title: Nonlinear autoregressive neural network and extended Kalman filters for prediction of financial time series
zbMATH Open document ID: 1442.62225
DOI: 10.1155/2020/5057801
author: Ghassane Benrhmach
author: Khalil Namir
author: Abdelwahed Namir
author: Jamal Bouyaghroumni
published in: Journal of Applied Mathematics
publication date: 2020-07-03T00:00:00Z
review text: Summary: Time series analysis and prediction are major scientific challenges that find their applications in fields as diverse as finance, biology, economics, meteorology, and so on. Obtaining the method with the least prediction error is one of the difficult problems of financial market and investment analysts. State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. The neural network is an important tool for analyzing time series especially when it is nonlinear and nonstationary. Essential tools for the study of Box-Jenkins methodology, neural networks, and extended Kalman filter were put together. We examine the use of the nonlinear autoregressive neural network method as a prediction technique for financial time series and the application of the extended Kalman filter algorithm to improve the accuracy of the model. As application on a real example, we are analyzing the time series of the daily price of steel over a 790-day period for establishing the superiority of this method over other existing methods. The simulation results using MATLAB and R software show that the model is capable of producing a reasonable accuracy.
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 91B84
zbMATH DE Number: 7217379
describes a project that uses: Matlab
describes a project that uses: R
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/5057801
OpenAlex ID: W3020059551
cites work: Q5663204
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cites work: Q5595902
cites work: Neural networks and physical systems with emergent collective computational abilities.
description: scientific article; zbMATH DE number 7217379