Name
Abstract | ||
|---|---|---|
Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting, there is so far no systematic research to study and compare their performance. How to select effective techniques of feature preprocessing in a forecasting model remains a problem. In this paper, the authors conduct a comprehensive study of existing feature preprocessing techniques to evaluate their empirical performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time series forecasting models. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11811305_84 | ADMA |
Keywords | Field | DocType |
data mining research,effective technique,comprehensive study,time series forecasting model,time series forecasting,preprocessing technique,forecasting model,empirical performance,systematic research,effective feature preprocessing,data mining,selection effect,computer science,artificial intelligence | Mean absolute percentage error,Time series,Data mining,Feature selection,Computer science,Support vector machine,Information extraction,Preprocessor,Artificial intelligence,Independent component analysis,Machine learning | Conference |
Volume | ISSN | ISBN |
4093 | 0302-9743 | 3-540-37025-0 |
Citations | PageRank | References |
2 | 0.36 | 13 |
Authors | ||
3 | ||