Forecasting Financial Time Series through Intrinsic Dimension Estimation and Non-Linear Data Projection

Abstract

A crucial problem in non-linear time series forecasting is to determine its auto-regressive order, in particular when the prediction method is non-linear. We show in this paper that this problem is related to the fractal dimension of the time series, and suggest using the Curvilinear Component Analysis (CCA) to project the data in a non-linear way on a space of adequately chosen dimension, before the prediction itself. The performances of this method are illustrated on the SBF 250 index.

DOI: 10.1007/BFb0100527

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@inproceedings{Verleysen1999ForecastingFT, title={Forecasting Financial Time Series through Intrinsic Dimension Estimation and Non-Linear Data Projection}, author={Michel Verleysen and Eric de Bodt and Amaury Lendasse}, booktitle={IWANN}, year={1999} }