Iterative time series prediction and analysis by embedding and multiple time-scale decomposition networks

In this work we describe a method of estimating and characterizing appropriate data and model complexity in the context of long term iterated time series forecasting using embeddings and multiple time-scale decomposition techniques. An embedding of a signal is obtained which decouples multiple time scale effects such as seasonality and trend. The complexity and stability of networks are estimated and the performance of long term iteration is examined. The performance of the technique is tested using the real world time series problems of electricity load forecasting, and financial futures contracts.