TY - GEN
T1 - Effective feature preprocessing for time series forecasting
AU - Zhao, Jun Hua
AU - Dong, Zhaoyang
AU - Xu, Zhao
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - 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. © Springer-Verlag Berlin Heidelberg 2006.
AB - 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. © Springer-Verlag Berlin Heidelberg 2006.
UR - http://www.scopus.com/inward/record.url?scp=33749379851&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-33749379851&origin=recordpage
U2 - 10.1007/11811305_84
DO - 10.1007/11811305_84
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3540370250
SN - 9783540370253
VL - 4093 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 769
EP - 781
BT - Advanced Data Mining and Applications - Second International Conference, ADMA 2006, Proceedings
PB - Springer Verlag
T2 - 2nd International Conference on Advanced Data Mining and Applications, ADMA 2006
Y2 - 14 August 2006 through 16 August 2006
ER -