In this update ACF and PACF are explained and the results which we got is being shared.
The snippet for ACF and PACF is:
The results which we got is:
PACF:
ACF:
For the ‘hotel_avg_daily_rate’, the Partial Autocorrelation Function (PACF) shows a significant initial spike at lag 1 with subsequent lags falling within the confidence interval, suggesting an AR(1) component for the SARIMA model. A progressive decrease in the Autocorrelation Function (ACF) suggests that differencing (d) should be included in order to attain stationarity and maybe a non-seasonal MA component.
In contrast, ‘hotel_occup_rate’ displays a strong initial spike in the PACF and significant seasonal spikes in the ACF, indicating potential seasonal MA components. This points to a SARIMA model with an AR(1) component and seasonal differencing, likely SARIMA(1,1,0)x(0,1,Q)12, where ‘Q’ corresponds to the significant seasonal lags observed in the ACF plot. The exact value for ‘Q’ would require further analysis of the seasonal lags, but the presence of clear seasonality suggests it would be non-zero.
In our next update, we will incorporate these values to our SARIMA model and discuss about the results we get.