Detecting Seasonality Through Autocorrelation

This post introduces and details methods for detecting seasonality in time series data, focusing on autocorrelation, ACF, and PACF. It explains the calculation and interpretation of autocorrelation values, including positive, negative, and zero correlations, as well as peak and multiple peaks. The post demonstrates these concepts using air passenger data, illustrating seasonality detection through line plots and seasonal decomposition (trend, seasonal, residual components). It provides Python code examples using pandas, matplotlib, and statsmodels, and links to a Hex notebook for hands-on exploration.