Forecasting the CATS benchmark with the Double Vector Quantization method

Authors:
Geoffroy Simon;John A. Lee;Marie Cottrell;Michel Verleysen
Affiliations:
Université catholique de Louvain, Machine Learning Group-DICE Place du Levant 3, B-1348 Louvain-la-Neuve, Belgium;Université catholique de Louvain, Machine Learning Group-DICE Place du Levant 3, B-1348 Louvain-la-Neuve, Belgium;Université Paris I-Panthéon Sorbonne, SAMOS-MATISSE, UMR CNRS 8595, Rue de Tolbiac 90, F-75634 Paris Cedex 13, France;Université catholique de Louvain, Machine Learning Group-DICE Place du Levant 3, B-1348 Louvain-la-Neuve, Belgium and Université Paris I-Panthéon Sorbonne, SAMOS-MATISSE, UMR CNRS 8 ...
Venue:
Neurocomputing
Year:
2007

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Abstract

The Double Vector Quantization (DVQ) method, a long-term forecasting method based on the self-organizing maps algorithm, has been used to predict the 100 missing values of the CATS competition data set. An analysis of the proposed time series is provided to estimate the dimension of the auto-regressive part of this nonlinear auto-regressive forecasting method. Based on this analysis experimental results using the DVQ method are presented and discussed. As one of the features of the DVQ method is its ability to predict scalars as well as vectors of values, the number of iterative predictions needed to reach the prediction horizon is further observed. The method stability for the long term allows obtaining reliable values for a rather long-term forecasting horizon.