Name
Title | ||
|---|---|---|
Integrated Time Series Forecasting approaches using moving average, grey prediction, support vector regression and bagging for NNGC |
Abstract | ||
|---|---|---|
Time series prediction is an interesting and challenging task in the field of data mining. This paper focuses on the monthly time series in NNGC. There are two main kinds of approaches, i.e. statistical approaches and computational intelligence approaches, which deal with time series prediction. We treat moving average and grey prediction from the statistical field as our benchmarks. We then combine these two approaches respectively with support vector regression (SVR) from the computational intelligence field. The hybrid SVR approaches outperform moving average and grey prediction based on the criteria of MAPE, SMAPE and RMSE. Finally, we further integrate these hybrid SVR approaches with the technique of the bagging ensembles to further achieve a better performance. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IJCNN.2010.5596798 | IJCNN |
Keywords | Field | DocType |
grey systems,grey prediction,rmse,svr,support vector regression,regression analysis,statistical approaches,mape,nngc,smape,data mining,bagging ensembles,bagging,time series,support vector machines,time series prediction,computational intelligence,time series forecasting,moving average | Data mining,Order of integration,Time series,Computer science,Regression analysis,Mean squared error,Artificial intelligence,Symmetric mean absolute percentage error,Computational intelligence,Pattern recognition,Support vector machine,Moving average,Machine learning | Conference |
ISSN | ISBN | Citations |
1098-7576 | 978-1-4244-6916-1 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chihli Hung | 1 | 259 | 16.93 |
| Xinyi Huang | 2 | 2245 | 129.63 |
| Hao-Kai Lin | 3 | 39 | 1.59 |
| Yen-Hsu Hou | 4 | 0 | 0.34 |