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Is it good to have no autocorrelation in Arima?
Also, no autocorrelation is not necessarily an indication that you have a model that will generalize well out of sample. You are likely to overfit when you require the residuals to have no autocorrelation. Meanwhile, AIC-based model selection as used in auto.arima strikes a good balance between underfitting and overfitting.
How to test the randomness of residuals from the ARIMA model?
Similar to the result for the ARIMA(2,1,0) model, it shows that for the first 25 lags, all sample autocorrelations expect those at lags 7 and 18 fall inside the 95% confidence bounds indicating the residuals appear to be random. Test the Randomness of Residuals From the ARIMA(0,1,1) Model Fit
What is the Box Ljung test for the ARIMA model?
The Box-Ljung test is also applied to the residuals from the ARIMA(0,1,1) model. The test indicates that there is at least one non-zero autocorrelation amont the first 24 lags. We conclude that there is not enough evidence to claim that the residuals are random (p-value = 0.026). Summary Overall, the ARIMA(0,1,1) is an adequate model.
Which is the best interpretation of the autocorrelation plot?
Interpretation of the Autocorrelation Plot The autocorrelation plot shows that for the first 25 lags, all sample autocorrelations except those at lags 7 and 18 fall inside the 95 % confidence bounds indicating the residuals appear to be random. Test the Randomness of Residuals From the ARIMA(2,1,0) Model Fit
Is the autocorrelation of residuals a problem?
Yes, autocorrelation in residuals is a problem, but this is essentially because it is a clear illustration that there was more learnable information in the process you are modelling but your model missed it.
How does auto.arima compute residuals for the first observation?
Update: Digging into the code of auto.arima, I see that it uses Arima which in turn uses stats:::arima. Therefore the question is really how does stats:::arima compute residuals for the very first observation?
When do you overfit with no autocorrelation?
You are likely to overfit when you require the residuals to have no autocorrelation. Meanwhile, AIC-based model selection as used in auto.arima strikes a good balance between underfitting and overfitting. Thanks for contributing an answer to Cross Validated!