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A Multiple Model Approach to Time-Series Prediction Using an Online Sequential Learning Algorithm
PES Univ, Dept Elect & Commun Engn, Bengaluru 560085, India.;PES Univ, PES Ctr Intelligent Syst, Bengaluru 560085, India..
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)In: IEEE Transactions on Systems, Man & Cybernetics. Systems, ISSN 2168-2216, E-ISSN 1349-2543, Vol. 49, no 5, p. 976-990Article in journal (Refereed) Published
Abstract [en]

Time-series prediction is important in diverse fields. Traditionally, methods for time-series prediction were based on fixed linear models because of mathematical tractability. Researchers turned their attention to artificial neural networks due to their better approximation capability. In this paper, we use feedforward neural networks with a single hidden layer, and present a rather simple online sequential learning algorithm (OSLA) together with its proof. The convergence properties of this algorithm are those of the well-known recursive least squares algorithm. We demonstrate that the prediction performance is better than other OSLAs, and show that it is statistically different from them. In addition, we also present the multiple models, switching, and tuning methodology that enhances the prediction performance of the learning algorithm.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2019. Vol. 49, no 5, p. 976-990
Keywords [en]
Prediction models, recurrent neural networks, supervised learning, time-series analysis
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:kth:diva-251477DOI: 10.1109/TSMC.2017.2712184ISI: 000464933200009Scopus ID: 2-s2.0-85023751074OAI: oai:DiVA.org:kth-251477DiVA, id: diva2:1315969
Note

QC 20190515

Available from: 2019-05-15 Created: 2019-05-15 Last updated: 2019-05-15Bibliographically approved

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Mutalik, Prabhanjan
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Citation style
  • apa
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  • vancouver
  • Other style
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  • de-DE
  • en-GB
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  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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