A Hybrid Model of Differential Evolution with Neural Network

What is it about?

The contribution of time series forecasting (TSF) on various aspects from economic to engineering has yielded its importance. Lot of recent studies concentrated on applying and modifying artificial neural network (ANN) to improve forecasting accuracy and achieved promising results. However, the selection of proper set from historical data for forecasting still has limited consideration. In addition, the selection of network structure as well as initial weights in ANN has been proved to have significant impact on the performance. This paper aims to propose a hybrid model that takes advantages of optimization algorithm: differential evolution (DE) in combine with ANN. The DE operates as features selection process that evaluates useful historical data known as lag to involve in learning process. Besides, DE will perform pre-calculation to determine the set of weight use for ANN. This proposed model is examined on agricultural commodity’s price to evaluate its accuracy. The experimental results is compared and surpassed the popular TSF technique autoregressive integrated moving average (ARIMA) and traditional multilayer perceptron (MLP).

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Photo by LAMBROS LYRARAKIS on Unsplash

Photo by LAMBROS LYRARAKIS on Unsplash

Why is it important?

The contribution of time series forecasting (TSF) on various aspects from economic to engineering has yielded its importance. Lot of recent studies concentrated on applying and modifying artificial neural network (ANN) to improve forecasting accuracy and achieved promising results. However, the selection of proper set from historical data for forecasting still has limited consideration. In addition, the selection of network structure as well as initial weights in ANN has been proved to have significant impact on the performance.

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