ACF and PACF

ACF (Autocorrelation Function) and PACF (Partial Autocorrelation Function) are tools used in time series analysis to understand data patterns over time. The ACF measures how current values relate to past values at different time gaps, showing overall correlations. The PACF, on the other hand, isolates the direct relationship between current values and past values, excluding the influence of intermediate points. Both help identify underlying structures in data, such as seasonal patterns or trends, guiding the selection of appropriate forecasting models.