Stat 5100 Handout #12.e – Notes: ARIMA Models (Unit 7) Key here: after stationary, identify dependence structure (and use for
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How should these data be modelled?. Identification step: Look at the SAC and SPAC Looks like an AR(1)- process. (Spikes are clearly decreasing in SAC. - ppt download
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Stat 5100 Handout #35 – Notes: ARIMA Models (Unit 8) Key here: after stationary, identify dependence structure (and use for fo
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![Figure 7. (a) RMSE auto.ARIMA vs STClu-Arima for Eastern-Wind data set for 30 cluster. (b) RMSE auto.ARIMA vs STClu-Arima for SAC data set for 90 clusters : Application of Spatio-Temporal Clustering in Figure 7. (a) RMSE auto.ARIMA vs STClu-Arima for Eastern-Wind data set for 30 cluster. (b) RMSE auto.ARIMA vs STClu-Arima for SAC data set for 90 clusters : Application of Spatio-Temporal Clustering in](http://pubs.sciepub.com/ajmo/2/1/2/image/fig7.png)