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Application of Data Mining in the Financial Data Forecasting

  • Conference paper
Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues (ICIC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5226))

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Abstract

A method of processing financial data based on wavelet transformation is presented. The data of the financial is essentially an unfixed time sequence. Based on the wavelet transform, the series obtained after decomposition contains information. Basically, the wavelet decomposition uses a pair of filters to decompose iteratively the original time series. It results in a hierarchy of new time series that are easier to model and predict. Regarded as a signal, the time sequence is decomposed into different frequency channels (as a filtering step) .These filters must satisfy some constraints such as causality, information lossless, etc. And reconstruction is used to analyze and forecast the time sequence. Examples show that the new method is more effective than the traditional AR model forecast in some aspects.

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Authors and Affiliations

  1. College of Teacher Education, Shanxi Normal University, Linfen, Shanxi, 041004, P.R. China

    Jie Wang

  2. College of Mathematics and Computer Science, Shanxi Normal University, Linfen, Shanxi, 041004, P.R. China

    Hong Wang

Authors
  1. Jie Wang
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  2. Hong Wang
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Editor information

Editors and Affiliations

  1. Department of Automation, University of Science and Technology of China, 230026, Hefei, Anhui, China

    De-Shuang Huang

  2. Applied Computational Intelligence Laboratory, P.O. Box

    Donald C. Wunsch II

  3. Arlington, USA

    Daniel S. Levine

  4. Graduate School of Electrical Engineering, University of Ulsan, Korea, San 29, Mugeo-Dong, 680 - 749, Nam-Ku, Ulsan, Korea

    Kang-Hyun Jo

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© 2008 Springer-Verlag Berlin Heidelberg

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Wang, J., Wang, H. (2008). Application of Data Mining in the Financial Data Forecasting. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_117

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  • DOI: https://doi.org/10.1007/978-3-540-87442-3_117

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87440-9

  • Online ISBN: 978-3-540-87442-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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