A Non-Parametric Algorithm to Estimate Future Samples in 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. In a more general sense the notion of null space refers to the set of eigenvectors of the data Hankel matrix which are associated with the smallest eigenvalues. The method is fully deterministic with comparable computational complexity to ESPRIT. Testing involves 4000 randomly chosen data sets with variable spectral characteristics.