To determine the order of the ARMA model for a given time series using the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF), you can follow these steps:

1. Identify the order of the Autoregressive (AR) part:
Look at the PACF plot. The order of the AR part is the lag after which the PACF cuts off. In other words, if the PACF plot shows a sharp cut-off while the ACF plot decays more slowly (i.e., has significant lags), you have an indication for the AR part.

2. Identify the order of the Moving Average (MA) part:
Look at the ACF plot. The order of the MA part is the lag after which the ACF cuts off. If the ACF plot shows a sharp cut-off and the PACF plot decays more slowly, you have an indication for the MA part.

3. Combine the AR and MA orders:
The orders obtained from the ACF and PACF plots give you the order of the ARMA model. For example, if the ACF cuts off after lag 1 and PACF cuts off after lag 2, an ARMA(1,2) model is suggested.

Remember, this is a rough estimation. The exact model order can be further refined using statistical tests and measures of fit such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC).

Question

To determine the order of the ARMA model for a given time series using the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF), you can follow these steps:

1. Identify the order of the Autoregressive (AR) part:
   Look at the PACF plot. The order of the AR part is the lag after which the PACF cuts off. In other words, if the PACF plot shows a sharp cut-off while the ACF plot decays more slowly (i.e., has significant lags), you have an indication for the AR part.

2. Identify the order of the Moving Average (MA) part:
   Look at the ACF plot. The order of the MA part is the lag after which the ACF cuts off. If the ACF plot shows a sharp cut-off and the PACF plot decays more slowly, you have an indication for the MA part.

3. Combine the AR and MA orders:
   The orders obtained from the ACF and PACF plots give you the order of the ARMA model. For example, if the ACF cuts off after lag 1 and PACF cuts off after lag 2, an ARMA(1,2) model is suggested.

Remember, this is a rough estimation. The exact model order can be further refined using statistical tests and measures of fit such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC).

Knowee AI · Accepted Answer

To determine the order of the ARMA model for a given time series using the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF), you can follow these steps:

1. Identify the order of the Autoregressive (AR) part:
   Look at the PACF plot. The order of the AR part is the lag after which the PACF cuts off. In other words, if the PACF plot shows a sharp cut-off while the ACF plot decays more slowly (i.e., has significant lags), you have an indication for the AR part.

2. Identify the order of the Moving Average (MA) part:
   Look at the ACF plot. The order of the MA part is the lag after which the ACF cuts off. If the ACF plot shows a sharp cut-off and the PACF plot decays more slowly, you have an indication for the MA part.

3. Combine the AR and MA orders:
   The orders obtained from the ACF and PACF plots give you the order of the ARMA model. For example, if the ACF cuts off after lag 1 and PACF cuts off after lag 2, an ARMA(1,2) model is suggested.

Remember, this is a rough estimation. The exact model order can be further refined using statistical tests and measures of fit such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC).

given the ACF and the PACF of the time series,, what ARMA model fits the time serius?

Question

Given the ACF and the PACF of the time series, what ARMA model fits the time series?

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