Affiliation:
1. Beihang University, China
2. Sichuan University and Chinese Academy of Sciences, China
3. 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.
Subject
Artificial Intelligence,Management of Technology and Innovation,Information Systems and Management,Organizational Behavior and Human Resource Management,Strategy and Management,Information Systems
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