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

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. Althou...

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Bibliographic Details
Published in:Computational intelligence and neuroscience 2019-01, Vol.2019 (2019), p.1-12
Main Authors: Miliic, Miljana, Milojkovic, Jelena, Markovic, Ivan, Nikolic, Petar
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Artificial intelligence
Artificial neural networks
Autoregressive models
Competitive advantage
Data Accuracy
Engineering
Forecasting
Forecasting techniques
Forecasts and trends
GNI
Gross National Income
Human error
Humans
Methodology
Methods
Neural networks
Neural Networks (Computer)
Neurosciences
Nonlinear Dynamics
Sales forecasting
Short term
Statistical analysis
Time series
Topology
Traffic flow
Uncertainty
Variation
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2019/9323482