NDLI: Wavelet Kernel Function for Stock Index Forecast

Content Provider	IEEE Xplore Digital Library
Author	Ling Bing Tang Huan Ye Sheng
Copyright Year	2009
Abstract	Stock index forecast plays an important role in finance.One of the challenging problems in forecasting the stock index is that general kernel functions in support vector machine (SVM) can’t capture no stationary characteristic of stock time series accurately. While wavelet function yields features that describe of the stock time series both at various locations and at varying time granularities, so this letter constructed a multidimensional wavelet kernel function and proved it meeting the mercer condition to address this problem. The applicability and validity of wavelet support vector machine (WSVM) for stock index forecasting were analyzed through experiments on real-world stock data. It appeared that the wavelet kernel is more accurate and performs better than the Gaussian kernel.
Starting Page	382
Ending Page	385
File Size	170583
Page Count	4
File Format	PDF
ISBN	9780769536439
DOI	10.1109/ISECS.2009.186
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multidimensional systems Finance Educational institutions svm Electronic commerce Support vector machines Computer science Neural networks Support vector machine classification wavelet kernel Kernel Computer security stock index
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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