01407nas a2200169   4500008004100000022001400041245008800055210006900143260008000212300001400292490000700306520074900313100001701062700002601079700002101105856011101126       2006                        eng d  a0305-447000aImproved time series prediction with a new method for selection of model parameters0 aImproved time series prediction with a new method for selection   aDIRAC HOUSE, TEMPLE BACK, BRISTOL BS1 6BE, ENGLANDbIOP PUBLISHING LTDcJUL  aL483-L4910 v393 a<p>A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Holder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data.</p>1 aJade, A., M.1 aJayaraman, Valadi, K.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/improved-time-series-prediction-new-method-selection-model-parameters-0