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A Combined Forecast Method Integrating Contextual Knowledge

Author

Listed:
  • Anqiang Huang

    (Beihang University, China)

  • Jin Xiao

    (Sichuan University and Chinese Academy of Sciences, China)

  • Shouyang Wang

    (Chinese Academy of Sciences, China)

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.

Suggested Citation

  • Anqiang Huang & Jin Xiao & Shouyang Wang, 2011. "A Combined Forecast Method Integrating Contextual Knowledge," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 2(4), pages 39-53, October.
    • Handle: RePEc:igg:jkss00:v:2:y:2011:i:4:p:39-53
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