In this update, we are going to explore more about SARIMA model and how it is going to be helpful for our project. SARIMA is a sophisticated time series forecasting model that we have been examining in our continuous investigation of the BPDA economic indicators dataset. Understanding the link between the Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and the SARIMA model itself is essential to using the model effectively.
ACF shows the correlation of a time series with itself, lagged by x time units. In simpler terms, it tells us how well the current value of the series is related to its past values. PACF , on the other hand, reveals the partial correlation of a time series with its own lagged values, controlling for the values at all shorter lags. It isolates the impact of each lag from the others.
Selecting AR (Autoregressive) terms (p in SARIMA): The PACF plot is instrumental in determining the order of the AR part of the SARIMA model. Significant spikes in the PACF plot indicate potential AR terms. For instance, if the PACF cuts off after lag p, this suggests an AR(p) model.
Selecting MA (Moving Average) terms (q in SARIMA): The ACF plot helps identify the appropriate number of MA terms. A significant spike at lag q in the ACF plot suggests a MA(q) term in the model.
Seasonal Elements (P, D, Q, s in SARIMA): Similar principles apply to the seasonal components of SARIMA. Seasonal spikes in these plots can help determine the P and Q terms, with the lag at which these spikes occur guiding the selection of s (seasonal period).
Using ACF and PACF studies on project columns like “hotel_occup_rate” and “avg_daily_rate” will help us understand their underlying patterns better and give us direction for creating our SARIMA models. For example, these results suggest that we integrate seasonal AR and non-seasonal MA components in our SARIMA model if the ‘hotel_occup_rate’ ACF plot has a clear seasonal pattern and the PACF plot displays an abrupt cut-off.
In the next update, we will discuss how the Python code for ACF and PACF works and how it is integrated to the SARIMA model.