In this update, we’re delving deeper into the SARIMA model’s application in analyzing the BPDA economic indicators dataset. Understanding the connections between Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and SARIMA is crucial for leveraging this model effectively. The ACF indicates how a time series correlates with its past values at different time lags, while the PACF reveals the isolated impact of each lag on the series, separate from shorter lags. Determining the SARIMA’s AR terms (p) relies on significant spikes in the PACF plot, signifying potential Autoregressive elements. Meanwhile, the ACF plot aids in identifying the suitable number of Moving Average terms (q) based on significant spikes at specific lags. Similar principles apply to seasonal aspects (P, D, Q, s) in SARIMA. Seasonal spikes in these plots help determine the seasonal components, with their positions guiding the selection of seasonal periods. Utilizing ACF and PACF analyses on columns like “hotel_occup_rate” and “avg_daily_rate” allows a better grasp of their inherent patterns, steering us toward crafting SARIMA models. For example, if the ACF plot of ‘hotel_occup_rate’ shows a clear seasonal pattern and the PACF plot demonstrates an abrupt cut-off, it suggests integrating seasonal AR and non-seasonal MA components into our SARIMA model.
The subsequent update will detail the functionality of Python code for ACF and PACF, demonstrating its integration with the SARIMA model.