Artificial neural networks for non-stationary time series

Tae Yoon Kim, Kyong Joo Oh, Chiho Kim, Jong Doo Do

Research output: Contribution to journalArticle

63 Citations (Scopus)

Abstract

The use of artificial neural networks (ANN) has received increasing attention in the analysis and prediction of financial time series. Stationarity of the observed financial time series is the basic underlying assumption in the practical application of ANN on financial time series. In this paper, we will investigate whether it is feasible to relax the stationarity condition to non-stationary time series. Our result discusses the range of complexities caused by non-stationary behavior and finds that overfitting by ANN could be useful in the analysis of such non-stationary complex financial time series.

Original languageEnglish
Pages (from-to)439-447
Number of pages9
JournalNeurocomputing
Volume61
Issue number1-4
DOIs
Publication statusPublished - 2004 Oct 1

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Time series
Neural networks

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Cellular and Molecular Neuroscience

Cite this

Kim, Tae Yoon ; Oh, Kyong Joo ; Kim, Chiho ; Do, Jong Doo. / Artificial neural networks for non-stationary time series. In: Neurocomputing. 2004 ; Vol. 61, No. 1-4. pp. 439-447.
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Artificial neural networks for non-stationary time series. / Kim, Tae Yoon; Oh, Kyong Joo; Kim, Chiho; Do, Jong Doo.

In: Neurocomputing, Vol. 61, No. 1-4, 01.10.2004, p. 439-447.

Research output: Contribution to journalArticle

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