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Computational Intelligence and Neuroscience
Volume 2019, Article ID 9323482, 12 pages
https://doi.org/10.1155/2019/9323482
Research Article

Concurrent, Performance-Based Methodology for Increasing the Accuracy and Certainty of Short-Term Neural Prediction Systems

1Faculty of Electronic Engineering, University of Niš, Aleksandra Medvedeva 14, 18000 Niš, Serbia
2Innovation Centre of Advanced Technologies, Bulevar Nikole Tesle 61, Loc. 5, 18000 Niš, Serbia
3Faculty of Economics, University of Niš, Trg Kralja Aleksandra Ujedinitelja 11, 18000 Niš, Serbia
4Tigar Tyres, Nikole Pašića 213, 18300 Pirot, Serbia

Correspondence should be addressed to Miljana Milić; sr.ca.in.kafle@cilim.anajlim

Received 28 December 2018; Revised 25 February 2019; Accepted 7 March 2019; Published 1 April 2019

Guest Editor: Vlado Delic

Copyright © 2019 Miljana Milić et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Accurate prediction of the short time series with highly irregular behavior is a challenging task found in many areas of modern science. Such data fluctuations are not systematic and hardly predictable. In recent years, artificial neural networks have widely been exploited for those purposes. Although it is possible to model nonlinear behavior of short time series by using ANNs, very often they are not able to handle all events equally well. Therefore, alternative approaches have to be applied. In this study, a new, concurrent, performance-based methodology that combines best ANN topologies in order to decrease the forecasting errors and increase the forecasting certainty is proposed. The proposed approach is verified on three different data sets: the Serbian Gross National Income time series, the municipal traffic flow for a particular observation point, and the daily electric load consumption time series. It is shown that the method can significantly increase the forecasting accuracy of the individual networks, regardless of their topologies, which makes the methodology more applicable. For quantitative comparison of the accuracy of the proposed methodology with that of similar methodologies, a series of additional forecasting experiments that include a state-of-the-art ARIMA modelling and a combination of ANN and linear regression forecasting have been conducted.