Role Of Partial Autocorrelation Function:

A SARIMA (Seasonal Autoregressive Integrated Moving Average) model's autoregressive (AR) component is sorted according to its order using a statistical technique known as partial autocorrelation function (PAF).

In a SARIMA model, the seasonal component reflects the effect of seasonality on the current value, while the moving average component (MA) and autoregressive component (AR) record the impact of previous forecasting mistakes on the current value.

The partial autocorrelation function (PAF), which accounts for the effects of all shorter lags, assesses the correlation between a time series and its own lagged values. In other words, after accounting for the effects of all shorter lags, the PAF at lag k measures the correlation between the time series and its own values lagged by k time units. Because it helps to assess how many lagged values of the time series are meaningful in predicting the present value, the PAF is helpful in determining the order of the AR component of a SARIMA model. The number of significant lags in the PAF plot determines the order of the AR component.

In order to increase the accuracy of the time series forecast, we can establish the proper order of the AR component in a SARIMA model by examining the PAF plot. The SARIMA models do have certain drawbacks, though. The fact that they might not be ideal for time series data with complicated patterns, such as non-linear patterns or several seasonal cycles, is one of their main drawbacks. These circumstances may call for more sophisticated time series models, including state space models or neural networks. SARIMA models may also be sensitive to the selection of hyperparameters, which could result in varying outcomes for various iterations of the algorithm.

To sum up, SARIMA is an effective and popular time series model for predicting future values of time series data. It is a useful tool for predicting recurring trends in time series data since it is specifically developed to capture seasonality in time series data. SARIMA models have significant drawbacks, but they are generally easy to use and comprehend, making them available to a wide range of users. SARIMA is a useful technique to have in your toolkit for projecting future values of time series data, regardless of your level of data analysis experience.