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Hi all,
in ARIMA analysis, how to choose P D Q values by using ACF and PACF plot..can anyone please give advise
Thanks,
Ram.
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In short, you can use the following rules to identify P (AR order), D (difference), Q (MA order) terms:
If the PACF of the differenced series displays a sharp cutoff and/or the lag-1 autocorrelation is positive--i.e., if the series appears slightly "underdifferenced"--then consider adding an AR term to the model. The lag at which the PACF cuts off is the indicated number of AR terms.
If the ACF of the differenced series displays a sharp cutoff and/or the lag-1 autocorrelation is negative--i.e., if the series appears slightly "overdifferenced"--then consider adding an MA term to the model. The lag at which the ACF cuts off is the indicated number of MA terms.
If there is a unit root in the AR part of the model--i.e., if the sum of the AR coefficients is almost exactly 1--you should reduce the number of AR terms by one and increase the order of differencing by one.
If there is a unit root in the MA part of the model--i.e., if the sum of the MA coefficients is almost exactly 1--you should reduce the number of MA terms by one and reduce the order of differencing by one.
This site gives more details on the topic https://people.duke.edu/~rnau/411arim3.htm
Check out the time series and forecasting tasks in SAS Studio! They provide an easy point-and-click interface for Time Series Data Preparation, Time Series Exploration, and Time Series Modeling and Forecasting.
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FYI, two more references:
https://www.otexts.org/fpp/8/7
https://onlinecourses.science.psu.edu/stat510/node/62
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