Time Series Forecasting using CCA and Kohonen Maps- Application to Electricity Consumption (2008)
A. Lendasse, J. Lee, V. Wertz, M. Verleysen, Av. G. Lemaître
Abstract. A general-purpose useful parameter in time series forecasting is the regressor, corresponding to the minimum number of variables necessary to forecast the future values of the time series....
Determination of the Mahalanobis Matrix using Nonparametric Noise Estimations (2008)
A. Lendasse, F. Corona, J. Hao, N. Reyhani, M. Verleysen
Abstract. In this paper, the problem of an optimal transformation of the input space for function approximation problems is addressed. The transformation is defined determining the Mahalanobis matrix...
Classification of investment funds by self-organizing maps (2007)
P. Cardon, A. Lendasse, V. Wertz, E. De Bodt, M. Verleysen, ...
An investment fund (or mutual fund) is an investment structure collecting money coming from individuals and investing according to preestablished objectives. Professional managers decide of the...
Application to the Bel 20 (2007)
A. Lendasse, E. De Bodt, V. Wertz, M. Verleysen
Non-linear financial time series forecasting –
A. Lendasse, M. Cottrell, V. Wertz, M. Verleysen
Abstract. The problem of electrical load forecasting presents some particularities, compared to the generic problem of time-series prediction. One of these particularities is that several values...
M. Verleysen, E. De Bodt, A. Lendasse
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...
In this paper we propose an effective procedure to reduce the computation time of a bootstrap approximation of the generalization error in a family of nonlinear regression models. The bootstrap [1]...
Should Seed Investors Read Business Plans? (2007)
D. Francois, B. Gailly, A. Lendasse, V. Wertz, M. Verleysen
A business plan is a document presenting in a concise form the key elements (management, finance, marketing, ...) describing a percieved business opportunity. It is used among others as a tool for...
\emph{Proc. of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)}, Honolulu, Hawai, Apr. 2007
Fast Bootstrap Methodology For Regression Model Selection (2005)
A. Lendasse, G. Simon, V. Wertz, M. Verleysen
Usii resampliA methodslih cross-valiqAkBR and bootstrapi a necessik i neural network desikA forsolvi; the problem of model structureselectiA; The bootstrapi a powerful method offerix a lowvariBTx of...
Vector Quantization: A Weighted Version For Time-Series Forecasting (2005)
A. Lendasse, D. Francois, V. Wertz, M. Verleysen
Nonlinear time-series prediction offers potential performance increases compared to linear models. Nevertheless, the enhanced complexity and computation time often prohibits an efficient use of...
Fast bootstrap for least-square support vector machines (2004)
A. Lendasse, G. Simon, V. Wertz, M. Verleysen
Abstract. The Bootstrap resampling method may be efficiently used to estimate the generalization error of nonlinear regression models, as artificial neural networks and especially Least-square...
Fast Approximation of the Bootstrap for Model Selection (2003)
G. Simon, A. Lendasse, V. Wertz, M. Verleysen
The bootstrap resampling method may be efficiently used to estimate the generalization error of a family of nonlinear regression models, as artificial neural networks. The main difficulty associated...
Approximation by Radial Basis Function Networks - Application to Option Pricing (2003)
A. Lendasse, J. Lee, E. De Bodt, V. Wertz, M. Verleysen
We propose a method of function approximation by radial basis function networks. We will demonstrate that this approximation method can be improved by a pre-treatment of data based on a linear model....
Nonlinear Time Series Prediction by Weighted Vector Quantization (2003)
A. Lendasse, D. Francois, V. Wertz, M. Verleysen
Classical nonlinear models for time series prediction exhibit improved capabilities compared to linear ones. Nonlinear regression has however drawbacks, such as overfitting and local minima problems,...
Input data reduction for the prediction of financial time series (2001)
A. Lendasse, J. Lee, E. Debodt, V. Wertz, M. Verleysen, ...
Abstract. Prediction of financial time series using artificial neural networks has been the subject of many publications, even if the predictability of financial series remains a subject of...