A Non-Parametric Algorithm for Predicting Future Samples in Single- and Multi-Channel Time Series

A new method to estimate future samples in time series data is presented and it is compared against the well known technique ESPRIT. It exploits the null space of the Hankel matrix of the data allowing the prediction of future samples with better accuracy and confidence. Moreover a generalization of the algorithm is derived that also applies to multichannel signals. Both cases with and without cross-channel coupling are considered and different algorithms are presented. The method is fully deterministic with comparable computational complexity to ESPRIT. Testing involves 4000 randomly chosen data sets with variable spectral characteristics.