Title: A machine learning based predictive model for time-series modelling and analysis

Authors: Qasem Abu Al-Haija

Addresses: Department of Data Science and Artificial Intelligence, Faculty of Information Technology, University of Petra, Amman, Jordan

Abstract: Time series modelling and forecasting is an essential field of supervised machine learning because of its appreciated contributions into numerous research and real-life applications including the corporate, commercial, science and engineering applications. Therefore, substantial contributions have been devoted to developing competent predictive models. In this paper, we propose an inclusive time-series predictive model using two modelling techniques, namely; multi-layer feed forward neural networks (FFNN) based delta learning rule model and nonlinear auto-regression neural network (NARX) based external input model. The developed models have been trained with least possible prediction error for the 10th order one step ahead predictor for FFNN model and the 50th order two-step ahead predictor for NARX model. As a case study, we have employed the stationary time-series for yearly averaged sunspot activity during the period from 1719-2018. To evaluate the performance of the predictive models, the models have been trained for more than 1000 epochs and have scored the maximum prediction accuracy of more than 99% after 405 epochs recording a mean square error of (2.2~6.5)× 10−2 for the training process. Eventually, the proposed models are considered comparative predictive model for any stationary time-series in several areas of study.

Keywords: data science; time-series; regression; forecasting; neural network; generalised delta rule; NARX model; autocorrelation function (AFC); cross-correlation function (CCF); prediction accuracy.

DOI: 10.1504/IJSTDS.2021.118782

International Journal of Spatio-Temporal Data Science, 2021 Vol.1 No.3, pp.270 - 283

Received: 25 Apr 2021
Accepted: 06 May 2021

Published online: 06 Nov 2021 *

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