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The Use of Domain Knowledge in Feature Construction for Financial Time Series Prediction

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Progress in Artificial Intelligence (EPIA 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2258))

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Abstract

Most of the existing data mining approaches to time series prediction use as training data an embed of the most recent values of the time series, following the traditional linear auto-regressive methodologies. However, in many time series prediction tasks the alternative approach that uses derivative features constructed from the raw data with the help of domain theories can produce significant prediction accuracy improvements. This is particularly noticeable when the available data includes multivariate information although the aim is still the prediction of one particular time series. This latter situation occurs frequently in financial time series prediction. This paper presents a method of feature construction based on domain knowledge that uses multivariate time series information. We show that this method improves the accuracy of next-day stock quotes prediction when compared with the traditional embed of historical values extracted from the original data.

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Author information

Authors and Affiliations

  1. Physics Department, Universidade da Beira Interior, 6200, Covilhã, Portugal

    Pedro de Almeida

  2. LIACC, Rua Campo Alegre, 823 - 2°, 4150, Porto, Portugal

    Luís Torgo

Authors
  1. Pedro de Almeida
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  2. Luís Torgo
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Editor information

Editors and Affiliations

  1. Faculty of Economics LIACC, Laboratório de Inteligência Artificial e Ciência de Computadores, University of Porto, Rua do Campo Alegre, 823, 4150-180, Porto, Portugal

    Pavel Brazdil  & Alípio Jorge  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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de Almeida, P., Torgo, L. (2001). The Use of Domain Knowledge in Feature Construction for Financial Time Series Prediction. In: Brazdil, P., Jorge, A. (eds) Progress in Artificial Intelligence. EPIA 2001. Lecture Notes in Computer Science(), vol 2258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45329-6_15

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  • DOI: https://doi.org/10.1007/3-540-45329-6_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43030-8

  • Online ISBN: 978-3-540-45329-1

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