NDLI: Training data selection by detecting predictability in non-stationary time series by a surrogate-cumulant based approach

Content Provider	IEEE Xplore Digital Library
Author	Deco, G. Schurmann, B.
Copyright Year	1996
Description	Author affiliation: Corp. Res. & Dev., Siemens AG, Munich, Germany (Deco, G.; Schurmann, B.)
Abstract	We introduce a nonparametric cumulant based statistical approach for detecting linear and nonlinear statistical dependences in nonstationary time series. The statistical dependence is detected by measuring the predictability which tests the null hypothesis of statistical independence, expressed in Fourier-space, by the surrogate method. Therefore, the predictability is defined as a higher-order cumulant based significance discriminating between the original data and a set of scrambled surrogate data which correspond to the null hypothesis of a noncausal relationship between past and present. Information about the predictability can be used for example to select regions where a temporal structure is visible in order to select data for training a neural network for prediction. The regions where only a noisy behavior is observed are therefore ignored, avoiding in this fashion the learning of irrelevant noise which normally spoils the generalization characteristics of the neural network. We present an example of nonstationarity given by the chaotic time series of Henon (1976) perturbed with linearly increasing additive Gaussian noise. Nonlinear structures are tested in financial time series, like the Dollar-DM Tick exchange rate.
Starting Page	11
Ending Page	19
File Size	539191
Page Count	9
File Format	PDF
ISBN	0818674563
DOI	10.1109/NICRSP.1996.542740
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-08-21
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Redundancy Neural networks Training data Tin Entropy Data mining Biological neural networks Research and development Testing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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