A nonparametric version of the Final Prediction Error (FPE) is analysed for lag selection in nonlinear autoregressive time series under very general conditions including heteroskedasticity. We prove consistency and derive probabilities of incorrect selections that have been previously unavailable. Since it is more likely to over®t (have too many lags) than to under®t (miss some lags), a correct...

Exploratory methods for determining appropriate lagged variables in a vector nonlinear time series model are investigated. The rst is a multivariate extension of the R statistic considered by Granger and Lin (1994), which is based on an estimate of the mutual information criterion. The second method uses Kendall's and partial statistics for lag determination. Both methods provide nonlinear anal...

in recent decades artificial neural networks (anns) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. this paper presents the application of artificial neural networks to predict drought in yazd meteorological station. in this research, different archite...

In this paper, we propose a new fixed-lag smoother that estimates the fixed-delayed state for a continuous-time stochastic system. The estimation error variance of the proposed smoother is minimized under the constraint that the estimated state converges to the real state exactly in finite time after noises or uncertainties disappear. For numerical computing, the proposed smoother is represente...

precipitation patterns are influenced by many factors, such as global atmospheric circulations to name but one. precipitation patterns in iran have always had great fluctuations even in a smaller scale like the alburz mountain range. the present research has tried to find the relationship between global atmospheric patterns and the pervasive precipitation ones in alburz. for doing so, 17 climat...

Nonpoint source (NPS) watershed projects often fail to meet expectations for water quality improvement because of lag time, the time elapsed between adoption of management changes and the detection of measurable improvement in water quality in the target water body. Even when management changes are well-designed and fully implemented, water quality monitoring efforts may not show definitive res...