I have established parameters for a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model to forecast the “hotel_avg_daily_rate” time series, taking into account the obvious seasonality in the data, using standard procedures and autocorrelation analysis (ACF and PACF plots). Finding ARIMA parameters (p, d, q) and seasonal parameters (P, D, Q, S) from PACF and ACF plots is the process of parameter selection.
The SARIMA model will be fitted to the training set using the chosen parameters, and model validation will entail predicting the test set and comparing results to the real values. Forecasts for the upcoming 12 months, including expected values, 95% confidence intervals, and the Root Mean Squared Error (RMSE) as a gauge of predictive accuracy, have been produced following the fitting of the SARIMA model. An indicator of how well the model predicts data is the RMSE, which is roughly 13.12. A lower value suggests a better fit.