arima arima -(Stableness test) According to the time series of scatter plots, autocorrelation coefficients and partial autocorrelation coefficients, unit root test (ADF), to determine the stability of the data; -(Model identification and ordering) Establish a corresponding time series model based on the identified features. After the smoothing process, if the partial autocorrelation function is censored, and the autocorrelation function is tailed, an AR model is established; if the partial autocorrelation function is tailed, and the autocorrelation function is truncated, it is established MA model; if both the partial autocorrelation function and the autocorrelation function are trailing, the sequence is suitable for the ARIMA model. You can use the BIC criterion to order the model and

Filename	Size	Update
R_ARIMA_Forecast.nb.html .pdf	617037	2020-04-30
R_ARIMA_Identify.nb.html .pdf	2843847	2020-04-30
R_VAR.nb.html .pdf	380352	2020-04-30
R_ARIMA.nb.html .pdf	826034	2020-04-30

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