Symmetric-embedding prediction of the CATS benchmark

Abstract

We present a general strategy for filling the missing data of the CATS benchmark time series prediction competition. Our approach builds upon a time-symmetric embedding of this time series and the use of a one-shot forecasting for each missing value inside the gaps from distant-enough delayed and forwarded predictors. In the extrapolation region we perform standard, non-iterated forward predictions. For modeling purposes we consider bagging of multi-layer perceptrons (MLPs). We discuss two different implementations of this strategy: The first one is based on a simultaneous modeling of both large- and short-scale dynamics information, using (suitably delayed and forwarded) original CATS values and their first differences as inputs to MLPs. The second one follows a two-stage strategy, in which behaviors at different scales are modeled separately. First, the overall behavior at large scales is fitted with a smooth curve obtained by repeated application of a Savitzky-Golay filter. Then, the remaining short-scale variability is approximated using bagged MLPs. Expected error levels for these two implementations are provided according to performance on test data.