Time series forecasting with SOM and local non-linear models - Application to the DAX30 index prediction

Abstract

A general method for time series forecasting is presented. Based on the splitting of the past dynamics into clusters, local models are built to capture the possible evolution of the series given the last\nknown values. A probabilistic model is used to combine the local predictions. The method can be applied to any time series prediction problem, but is particularly suited to data showing non-linear dependencies\nand cluster effects, as many financial series do. The method is applied to the prediction of the returns of the DAX30 index.