Effective feature preprocessing for time series forecasting

Zhao, Jun Hua, Dong, ZhaoYang and Xu, Zhao (2006). Effective feature preprocessing for time series forecasting. In: J. G. Carbonell and J. Siekmann, Lecture Notes in Computer Science: Advanced Data Mining And Applications. 2nd International Conference in Advanced Data Mining and Applications, Xi'an, China, (769-781). 14-16 August, 2006.


Author Zhao, Jun Hua
Dong, ZhaoYang
Xu, Zhao
Title of paper Effective feature preprocessing for time series forecasting
Conference Paper Type Fully Published Paper
Conference name 2nd International Conference in Advanced Data Mining and Applications    (ERA 2010 Rank B)
DOI 10.1007/11811305
Conference location Xi'an, China
Conference dates 14-16 August, 2006
Proceedings title Lecture Notes in Computer Science: Advanced Data Mining And Applications  (ERA 2012 Listed)   Check publisher's open access policy
Journal name Advanced Data Mining and Applications, Proceedings  (ERA 2012 Listed)   Check publisher's open access policy
Editor J. G. Carbonell
J. Siekmann
Place published Berlin
Publisher Springer-Verlag Berlin
Publication date 2006
Volume number 4093
ISBN 3-540-37025-0
ISSN 0302-9743
1611-3349
Start page 769
End page 781
Total pages 13
Collection year 2006
Language eng
Formatted Abstract/Summary 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.
Subjects E1
280100 Information Systems
700103 Information processing services
Keyword Time series forecasting
preprocessing techniques
Computer Science
Artificial Intelligence
Q-Index Code E1

 
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Created: Thu, 23 Aug 2007, 21:44:56 EST