Effective feature preprocessing for time series forecasting

Jun Hua Zhao, Zhaoyang Dong, Zhao Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - Second International Conference, ADMA 2006, Proceedings
PublisherSpringer Verlag
Number of pages13
ISBN (Electronic)9783540370253
ISBN (Print)3540370250
Publication statusPublished - 1 Jan 2006
Externally publishedYes
Event2nd International Conference on Advanced Data Mining and Applications, ADMA 2006 - Xi'an, China
Duration: 14 Aug 200616 Aug 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4093 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd International Conference on Advanced Data Mining and Applications, ADMA 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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