Forecasting Issues: Ideas of Decomposition and Combination

Combination techniques and decomposition procedures have been applied to time series forecasting to enhance prediction accuracy and to facilitate the analysis of data respectively. However, the restrictive complexity of some combination techniques and the difficulties associated with the application of the decomposition results to the extrapolation of data, mainly due to the large variability involved in economic and financial time series, have limited their application and compromised their development. This paper is a re-examination of the benefits and limitations of decomposition and combination techniques in the area of forecasting, and a contribution to the field with a new forecasting methodology. The new methodology is based on the disaggregation of time series components through the STL decomposition procedure, the extrapolation of linear combinations of the disaggregated sub-series, and the reaggregation of the extrapolations to obtain estimation for the global series. With the application of the methodology to the data from the NN3 and M1 Competition series, the results suggest that it can outperform other competing statistical techniques. The power of the method lies in its ability to perform consistently well, irrespective of the characteristics, underlying structure and level of noise of the data.