Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition
This work addresses forecasting accuracy for decision-makers in various domains, but it is incremental as it builds on existing hybrid methods.
The paper tackled the problem of improving time series forecasting accuracy by introducing a new ARIMA-ANN hybrid method with empirical mode decomposition, resulting in enhanced performance over traditional hybrid and individual methods.
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 which 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 appropriate strategies, our hybrid method can be an effective way to improve forecasting accuracy obtained by traditional hybrid methods and also either of the individual methods used separately.