NDLI: Novel time series analysis and prediction of stock trading using fractal theory and time delayed neural network

Content Provider	IEEE Xplore Digital Library
Author	Yakuwa, F. Dote, Y. Yoneyama, M. Uzurabashi, S.
Copyright Year	2003
Description	Author affiliation: Hokkaido Electr. Power Co. Inc., Kushiro, Japan (Yakuwa, F.)
Abstract	The stock markets are well known for wide variations in prices over short and long terms. These fluctuations are due to a large number of deals produced by agents and act independently from each other. However, even in the middle of the apparently chaotic world, there are opportunities for making good predictions. In this paper the Nikkei stock prices over 1500 days from July to Oct. 2002 are analyzed and predicted using a Hurst exponent (H), a fractal dimension (D), and an autocorrelation coefficient (C). They are H=0.6699 D=2-H=1.3301 and C=0.26558 over three days. This obtained knowledge is embedded into the structure of our developed time delayed neural network. It is confirmed that the obtained prediction accuracy is much higher than that by a back propagation-type forward neural network for the short-term. Although this predictor works for the short term, it is embedded into our developed fuzzy neural network to construct multi-blended local nonlinear models. It is applied to general long term prediction whose more accurate prediction is expected than that by the method proposed.
Sponsorship	Syst., Man & Cybernetics Soc. IEEE
Starting Page	134
Ending Page	141
File Size	404242
Page Count	8
File Format	PDF
ISBN	0780379527
ISSN	1062922X
DOI	10.1109/ICSMC.2003.1243804
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Fractals Delay effects Neural networks Stock markets Fluctuations Chaos Autocorrelation Accuracy Fuzzy neural networks
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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