Input Selection for Long-Term Prediction of Time Series

Prediction of time series is an important problem in many areas of science and engineering. Extending the horizon of predictions further to the future is the challenging and difficult task of long-term prediction. In this paper, we investigate the problem of selecting noncontiguous input variables for an autoregressive prediction model in order to improve the prediction ability. We present an algorithm in the spirit of backward selection which removes variables sequentially from the prediction models based on the significance of the individual regressors. We successfully test the algorithm with a non-linear system by selecting inputs with a linear model and finally train a non-linear predictor with the selected variables on Santa Fe laser data set.