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Artificial neural networks for non-stationary time series

Authors
Journal
Neurocomputing
0925-2312
Publisher
Elsevier
Publication Date
Volume
61
Identifiers
DOI: 10.1016/j.neucom.2004.04.002
Keywords
  • Non-Stationary Time Series
  • Overfitting
  • Artificial Neural Networks
  • Asymptotic Stationary Autoregressive Model
Disciplines
  • Computer Science

Abstract

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.

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