Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition

Abstract

Many applications in different domains produce large amount of time series data. Making accurate forecasting is critical for many decision makers. Various time series forecasting methods exist that use linear and nonlinear models separately or combination of both. Studies show that combining of linear and nonlinear models can be effective to improve forecasting performance. However, some assumptions that those existing methods make, might restrict their performance in certain situations. We provide a new Autoregressive Integrated Moving Average (ARIMA)-Artificial Neural Network (ANN) hybrid method that work in a more general framework. Experimental results show that strategies for decomposing the original data and for combining linear and nonlinear models throughout the hybridization process are key factors in the forecasting performance of the methods. By using these findings, the proposed hybrid method is combined with Empirical Mode Decomposition (EMD) technique which generates more predictable components. We show that our hybrid method with EMD can be an effective way to improve forecasting accuracy obtained by traditional hybrid methods and also any of the individual methods that we used separately.