Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-87442-3_117 | ICIC (1) |
Keywords | Field | DocType |
traditional ar model forecast,information lossless,financial data forecasting,data mining,new time series,financial data,wavelet transformation,new method,wavelet decomposition,time sequence,unfixed time sequence,original time series,ar model,time series,satisfiability,wavelet transform | Data mining,Computer science,Continuous wavelet transform,Artificial intelligence,Discrete wavelet transform,Wavelet packet decomposition,Wavelet transform,Wavelet,Second-generation wavelet transform,Cascade algorithm,Finance,Stationary wavelet transform,Machine learning | Conference |
Volume | ISSN | Citations |
5226 | 0302-9743 | 1 |
PageRank | References | Authors |
0.40 | 2 | 2 |