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

Miljana Milić,Petar Nikolić,Jelena Milojković,Ivan Marković

doi:10.1155/2019/9323482

Miljana Milić, Petar Nikolić + Show 2 more

Open Access

https://doi.org/10.1155/2019/9323482

Copy DOI

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.

Highlights

Prediction is a process that uses data from the present and the past in order to estimate future. e result of this process is the information about probable events in the future and their effects and outcomes
A novel methodology for increasing the predictions accuracy of different Artificial neural networks (ANN)-based systems has been suggested. roughout analysis of three different time series of important everyday parameters, we have introduced some efficient improvements for prediction of short time series
E proposed method has been verified on Gross National Income (GNI) forecasting at national economy level, municipal traffic volume forecasting, and suburban daily electric load consumption forecasting

Summary

Introduction

Prediction is a process that uses data from the present and the past in order to estimate future. e result of this process is the information about probable events in the future and their effects and outcomes. Prediction is a process that uses data from the present and the past in order to estimate future. E result of this process is the information about probable events in the future and their effects and outcomes. Making good forecasts is essential for making good decisions and planning in all areas of life. E need for development of prediction methods occurs in almost every area of life—technology, engineering, industry, science, politics, economy, business, sport, medicine, etc. E planning begins with a prediction [2]. Prediction errors may have crucial implications on decision-making, profits and investment justification, risk assessment, alerting events, hard real-time systems’ actions, timely handling of emergency health and medical conditions, etc. Because of that, decreasing the error of the prediction is an essential task for every forecasting expert, regardless of the applied prediction methods

Methods

Findings

Discussion

Conclusion