Neural networks for pattern recognition
Neural networks for pattern recognition
Nonlinear time-series prediction with missing and noisy data
Neural Computation
On incorporating seasonal information on recursive time series predictors
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Analysis of fast input selection: application in time series prediction
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Fast variable selection for memetracker phrases time series prediction
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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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 non-contiguous 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.