Above is 2 years and 4 months of actual demand data for a new call center.For example, cell B4 represenets 12,380 calls received in January 2019.Using this data, create two different models for demand forecasting.One combining Exponential Smoothing with Trend, and one combing Exponential Smoothing with both Trend and Seasonality. Ensure you conduct some type of error analysis and try to input adjust factors (such as Alpha) to minimize error.Please clearly indicate at the top of your sheet your selected value for factors (Alpha, Beta, Gamma) and the error analysis metric name and value. In the space below, indicate which model has less error and what you believe that indicates is true about the underlying demand pattern.

2019 2020 2021 Month January February|March April May June July August September October November December January |February| March April May June July August September October November December January February March April Actual Demand 12,380 9,920 12,450 13,420 15,920 18,380 21,110 21,500 18,110 15,450 13,060 13,630 13,550 13,160 13,340 14,850 16,080 20,480 2 21,700 23,130 17,280 14,240 14,450 15,583 13,818 15,208 16,038 Above is 2 years and 4 months of actual demand data for a new call center. For example, cell B4 represenets 12,380 calls received in January 2019. Using this data, create two different models for demand forecasting. One combining Exponential Smoothing with Trend, and one combing Exponential Smoothing with both Trend and Seasonality. Ensure you conduct some type of error analysis and try to input adjust factors (such as Alpha) to minimize error. Please clearly indicate at the top of your sheet your selected value for factors (Alpha, Beta, Gamma) and the error analysis metric name and value. In the space below, indicate which model has less error and what you believe that indicates is true about the underlying demand pattern