Time Series Forecasting Using Empirical Mode Decomposition and Hybrid Method

Büyükşahin, Ümit Çavuş
Ertekin Bolelli, Şeyda
Recently, various applications produce large amount of time series data. In these domains, accurately forecasting time series has been getting important for decision makers. autoregressive integrated moving average (ARIMA) as a linear method and Artificial Neural Networks (ANNs) as a nonlinear method have been widely used to forecast time series. However, many theoretical and empirical studies showed that assembling of those two approaches in hybrid methods can be efficient to improve forecasting performance by alleviating volatility problem in time series. Rather than two components, the developed method decomposes data into relatively stationary multiple components by using Empirical Mode Decomposition (EMD). Then each of them are separately modelled by the hybrid method proposed by Zhang which shows great success in time series forecasting as compared to well known methods. The evaluation of the developed method is performed on three different publicly available benchmark dataset and achieved highly successful results as compared to existing well-accepted hybrid methods.