4th Dec 2023

The Autocorrelation Function (ACF) plot is a visual tool used in time series analysis to depict the correlation between a time series and its lagged values. In this plot, the x-axis represents the time lags, indicating how many time points back the correlation is calculated, while the y-axis represents the correlation coefficients. ACF plots are essential for uncovering patterns and relationships within the time series data. Peaks or troughs in the plot indicate the strength and direction of these correlations, helping analysts identify potential seasonality or repeating trends in the dataset.

The ACF plot plays a critical role in model selection, particularly in the development of Autoregressive Integrated Moving Average (ARIMA) models. By examining the decay or persistence of autocorrelations in the plot, analysts can infer the order of autoregressive (AR) and moving average (MA) components. This information is instrumental in constructing accurate and effective time series models for forecasting and understanding the underlying dynamics of the data.