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An evolutionary approach to time series forecasting with artificial neural networks
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this paper an evolutionary approach to forecasting the stock market is tested and compared with backpropagation. An neuroevolutionary algorithm is implemented and backtested measuring returns and the normalized-mean-square-error for each algorithm on selected stocks from NASDAQ. The results are not entirely conclusive and further investigation would be needed to say definitely, but it seems as a neuroevolutionary approach could outperform backpropagation for time series prediction.

Place, publisher, year, edition, pages
2015.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-168224OAI: oai:DiVA.org:kth-168224DiVA: diva2:815021
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Available from: 2015-05-29 Created: 2015-05-29 Last updated: 2015-05-29Bibliographically approved

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fulltext(966 kB)590 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • de-DE
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Output format
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