Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.4018/jkss.2011100104 | IJKSS |
Keywords | Field | DocType |
single model,exponential smoothing,forecast error,irregular event,combined forecast method,combined forecast method integrating,forecast model,contextual knowledge,container throughput forecast case | Exponential smoothing,Data mining,Computer science,Knowledge management,Autoregressive integrated moving average,Throughput,Forecast error | Journal |
Volume | Issue | ISSN |
2 | 4 | 1947-8208 |
Citations | PageRank | References |
3 | 0.49 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shouyang Wang | 1 | 2396 | 219.80 |
| Anqiang Huang | 2 | 5 | 1.88 |
| Jin Xiao | 3 | 80 | 8.89 |