Showing papers by "Amaury Lendasse published in 2000"

Non-linear financial time series forecasting application to the Bel 20 stock market index

Abstract: We developed in this paper a method to predict time series with non-linear tools. The specificity of the method is to use as much information as possible as input to the model (many past values of the series, many exogenous variables), to compress this information (by a non-linear method) in order to obtain a state vector of limited size, facilitating the subsequent regression and the generalization ability of the forecasting algorithm and to fit a non-linear regressor (here a RBF neural network) on the reduced vectors. We show that this method is able to find non-linear relationships in artificial and real-world financial series. On a difficult task, which consists in forecasting the tendency of the Bel 20 stock market index, we show that this method improves the results compared both to linear models and to non-linear ones where the non-linear compression is not used.

A robust nonlinear projection method

Abstract: This paper describes a new nonlinear projection method. The aim is to design a user-friendly method, tentativ ely as easy to use as the linear PCA (Principal Component Analysis). The method is based on CCA (Curvilinear Component Analysis). This paper presen ts tw o improvements with respect to the original CCA: a better beha vior in the projection of highly nonlinear databases (like spirals) and a complete automation in the choice of the parameters value.

A robust non-linear projection method.

Abstract: This paper describes a new nonlinear projection method. The aim is to design a user-friendly method, tentativ ely as easy to use as the linear PCA (Principal Component Analysis). The method is based on CCA (Curvilinear Component Analysis). This paper presen ts tw o improvements with respect to the original CCA: a better beha vior in the projection of highly nonlinear databases (like spirals) and a complete automation in the choice of the parameters value.

Time Series Forecasting using CCA and Kohonen Maps - Applications to Electricity Consumption

Abstract: Using large regressors in non-linear time series forecasting makes the fitting of the model difficult. This paper shows how to reduce the size of regressors in order to improve the forecasting performances, using the Curvilinear Component Analysis as projection tool. The method is applied to the Polish electrical load forecasting.