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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Casdagli, M.: Nonlinear Prediction of Chaotic Time Series. Physica D 35, 335–356 (1989)
Vieu, P.: Order Choice in Nonlinear Autoregressive Models, vol. 26, pp. 304–328 (1995)
Li, Z.C., Luo, J.S.: Wavelet Analysis and Its Application. Electronics industry publishing company, Beijing (2003)
Zhao, K., Wang, Z.H.: Wavelet Analysis and Its in Analytical Chemistry Application. Geological Press, Beijing (2000)
Soltani, S., Boichu, D., Simard, P., Canu, S.: The Long-Term Memory Prediction by Multiscale Decomposition. Signal Processing 80, 2195–2205 (2000)
Xu, K., Xu, J.W., Ban, X.J.: Based on Wavelet Certain Non- Steady Time Series Forecast Methods. Electronic journal 29, 566–568 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)