A Novel Time Series Forecasting Approach Considering Data Characteristics

Ling Tang, Shuai Wang and Lean Yu ()
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Ling Tang: School of Economics and Management, Beijing University of Chemical Technology, Beijing, China
Shuai Wang: NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China

International Journal of Knowledge and Systems Science (IJKSS), 2014, vol. 5, issue 3, 46-53

Abstract: A novel time series forecasting approach with consideration of inner knowledge hidden in data, in terms of data characteristics, is proposed. In the proposed methodology, the main data characteristics hidden in the observed time series data are first explored; and according to the data characteristics, suitable forecasting models are formulated to improve prediction performance. For illustration, the proposed methodology is used to predict Chinese total social consumption and total energy consumption. The empirical results show the forecasting model considering data characteristics outperforms other popular forecasting models ignoring data characteristics, which further implies that data characteristics exploration is an important and necessary step in forecasting and the proposed methodology can be used as a promising approach for time series forecasting.

Date: 2014
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