Well-known Box-Jenkins Autoregressive integrated moving average (ARIMA) methodology has virtually dominated analysis of time-series data since 1930s. However, it is applicable to only those data that are either stationary or can be made so. Another limitation is that the resultant model is “Linear”. During the last two decades or so, the area of “Nonlinear time-series” is rapidly growing. Here,...

comparison of linear and nonlinear (bilinear) time series models in reference crop evapotranspiration prediction in urmia synoptic stationreference crop evapotranspiration (eto) prediction is one of the important elements in optimizing agricultural water consumption. in this regard, one of the prediction approaches is to use the stochastic time series methods. in this research, ar (p) and arma(...

Principal component analysis (PCA) and kernel PCA allow the decorrelation of data with respect to a basis that is found via variance maximization. However, these techniques are based on pointwise correlations. Especially in context time series this not optimal. We present novel generalization allows imprint any desired correlation pattern. Thus proposed method can be used incorporate previously...