Forecasting the CATS benchmark with the Double Vector Quantization method

Details Forecasting the CATS benchmark with the Double Vector Quantization method Forecasting the CATS benchmark with the Double Vector Quantization method

2007-03-06

arxiv.org/abs/math/0703143v1

Mathematics

Statistics Theory

Accepted for publication in Neurocomputing, Elsevier

Scientific paper

The Double Vector Quantization method, a long-term forecasting method based on the SOM 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 Double Vector Quantization (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.

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