A Combined Forecast Method Integrating Contextual Knowledge

Authors:
Shouyang Wang;Anqiang Huang;Jin Xiao
Affiliations:
Chinese Academy of Sciences, China;Beihang University, China;Sichuan University and Chinese Academy of Sciences, China
Venue:
International Journal of Knowledge and Systems Science
Year:
2011

Citing 6
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Abstract

In the framework of TEI@I methodology, this paper proposes a combined forecast method integrating contextual knowledge CFMIK. With the help of contextual knowledge, this method considers the effects of those factors that cannot be explicitly included in the forecast model, and thus it can efficiently decrease the forecast error resulted from the irregular events. Through a container throughput forecast case, this paper compares the performance of CFMIK, AFTER a combined forecast method and 3 types of single models ARIMA, BP-ANN, exponential smoothing. The results show that the performance of CFMIK is better than that of the others.