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.Comment: Accepted for publication in Neurocomputing, Elsevie
