PROBLEM 2: A chain operates a hotel in a large metropolitan area. You have been asked to develop a model that could be used to obtain short-term forecasts (up to 1 year) of the number of occupied rooms in the hotel. These forecasts are needed by various professional staff to assist in decisions regarding hiring additional help, ordering materials and supplies that have long delivery lead times, etc. Consider the monthly occupancy rate (daily average during a month) for last 15 years (January 2008 to December 2021) given in EXCEL file (HOTEL_occupancy.XLSX). Create an EXCEL file to test Multiplicative Seasonal Exponential Smoothing Model(s) by using following steps: a. Use first two years of data to estimate initial (time 0) Level (L0), Trend (T0), and the seasonal factors (if any) for each month of the year. b. Using smoothing constants , create an EXCEL sheet to apply model to your data and compute Mean % Error, Mean Absolute % Error, RMSE (Root Mean Square Error), and other indicators. Discuss what information these indicators provide. Use your model to forecast demand for next 12 months. c. Use SOLVER to find optimum values for smoothing constants (restricted to be between 0.1 and 0.4) that minimizes RMSE. Use your model to forecast demand for next 12 months. d. Use SPSS to test appropriate exponential smoothing models (with no restriction on the values of exponential smoothing constants ( ). Compare it to model in part (b) and part (c). Under what conditions you will prefer one over the other. e. Use Expert Modeler in SPSS to find best ARIMA model. What is the forecasting model? f. Based on your answer to part (b), (c), (d) and (e), what model will you recommend? What will be your forecast for next 12 months?