Forecasting financial time series through intrinsic dimension estimation and non-linear data projection
(1999) International Workshop on Artificial and Natural Neural Networks — Location: Alicante (Spain) (2.June.1999)
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- 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.
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Citations
Lendasse, A., de Bodt, E., & Verleysen, M. (1999). Forecasting financial time series through intrinsic dimension estimation and non-linear data projection. In J. Mira, J. Sanchez-Andres eds. (ed.), Engineering Applications of Bio-Inspired Artificial Neural Networks (pp. II596-II605). Springer. https://doi.org/10.1007/BFb0100527