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A Combined Forecast Method Integrating Contextual Knowledge

A Combined Forecast Method Integrating Contextual Knowledge

Anqiang Huang, Jin Xiao, Shouyang Wang
Copyright: © 2011 |Volume: 2 |Issue: 4 |Pages: 15
ISSN: 1947-8208|EISSN: 1947-8216|EISBN13: 9781613508145|DOI: 10.4018/jkss.2011100104
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MLA

Huang, Anqiang, et al. "A Combined Forecast Method Integrating Contextual Knowledge." IJKSS vol.2, no.4 2011: pp.39-53. http://doi.org/10.4018/jkss.2011100104

APA

Huang, A., Xiao, J., & Wang, S. (2011). A Combined Forecast Method Integrating Contextual Knowledge. International Journal of Knowledge and Systems Science (IJKSS), 2(4), 39-53. http://doi.org/10.4018/jkss.2011100104

Chicago

Huang, Anqiang, Jin Xiao, and Shouyang Wang. "A Combined Forecast Method Integrating Contextual Knowledge," International Journal of Knowledge and Systems Science (IJKSS) 2, no.4: 39-53. http://doi.org/10.4018/jkss.2011100104

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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.

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