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

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.

• Type: Conference
• Year: 1999
• Where: IWANN
• Authors: Michel Verleysen, Eric de Bodt, Amaury Lendasse
• Keywords: Curvilinear Component Analysis, IWANN 1999, Neural Networks, Non-linear Time Series, Prediction Method