After addressing time series components and stationarity, the focus shifts to understanding the internal correlation structure of the data. How does a value at time $t$ relate to values at previous times like $t-1$, $t-2$, and so on? Answering this is essential for selecting appropriate forecasting models.

This chapter introduces the primary tools for analyzing this temporal dependence:

• Autocorrelation Function (ACF): Learn how it measures the correlation between a time series and its lagged versions.
• Partial Autocorrelation Function (PACF): Understand how it measures the correlation between a time series and a lagged version after removing the influence of intermediate lags.
• Plotting and Interpretation: See how to generate ACF and PACF plots using Python libraries and learn standard patterns that suggest specific model types (like AR or MA models).

By the end of this chapter, you will be able to compute and interpret ACF and PACF plots to help identify potential candidate models for your stationary time series data.