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

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.

Optimal Neural Network Model for Short-Term Prediction of Confirmed Cases in the COVID-19 Pandemic

“…If a specific hypothesis is set incorrectly due to bad assessment, i.e., human error, the forecast will be incorrect. Although predicting is based on past occurrences, no one can ensure that history will repeat itself in the same way every time [12]. Sometimes, the key reason for the limitation of the ANN based learning i.e., low accuracy is the random initialization of the network, and the applications of some preselected neural structures with a fixed number of neurons in the network's layers.…”