Double quantization forecasting method for filling missing data in the CATS time series

The double vector quantization forecasting method based on Kohonen self-organizing maps is applied to predict the missing values of the CATS competition data set. As one of the features of the method is the ability to predict vectors instead of scalar values in a single step, the compromise between the size of the vector prediction and the number of repetitions needed to reach the required prediction horizon is studied. The long-term stability of the double vector quantization method makes it possible to obtain reliable values on a rather long-term forecasting horizon.