Smoothing Techniques for Prediction of Non-Linear Time Series
Svetlana Borovkova, Robert Main Burton, Herold Dehling · Applied and numerical harmonic analysis · 1998
We address the problem of non-linear modelling of time series and give a brief introduction to the method of state space reconstruction — embedding one-dimensional data into a higher-dimensional space. This method is based on the fundamental result in the area of chaotic time series, the Takens reconstruction theorem. Then we consider, among other non-linear methods of prediction, the kernel estimation of autoregression, and introduce a variation of this method. We apply it to an experimental time series and compare its performance with predictions by feed-forward neural networks as well as with fitting a local and a global linear autoregression. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.