The ACF plot now decays and the PACF plot again cuts off after lag 1. Thus an AR(1) model applied to the first differences might be an adequate model for these data. The other thing to note before fitting the ARIMA(1,1,0) model is that the mean of the differenced time series is not close to 0, thus an intercept term should be used in the model. Often times a differenced time series will have a mean close to 0 and thus an intercept may not be needed in the ARIMA model. You fit the ARIMA(1,1,0) model from the plot of the ORIGINAL time series not the first differenced one. The order of differencing (d) is specified when fitting the ARIMA model in JMP. The dialogue box for the ARIMA(1,1,0) model for these data is shown below. The fitted model is summarized below. The model indicating a very good fit. The residuals are shown below. The residuals look fine, so we could then use this model to obtain forecasts etc. Example 4: DVD (DVD Sales.JMP)
The ACF plot now decays and the PACF plot again cuts off after lag 1. Thus an AR(1) model applied to the first differences might be an adequate model for these data. The other thing to note before fitting the ARIMA(1,1,0) model is that the mean of the differenced time series is not close to 0, thus an intercept term should be used in the model. Often times a differenced time series will have a mean close to 0 and thus an intercept may not be needed in the ARIMA model. You fit the ARIMA(1,1,0) model from the plot of the ORIGINAL time series not the first differenced one. The order of differencing (d) is specified when fitting the ARIMA model in JMP. The dialogue box for the ARIMA(1,1,0) model for these data is shown below. The fitted model is summarized below. The model indicating a very good fit. The residuals are shown below. The residuals look fine, so we could then use this model to obtain forecasts etc. Example 4: DVD (DVD Sales.JMP)