6. Suppose that four of the years five events are complete and you have actual attendance data for those events. Use the MAD metric to determine which of your forecasting models is most appropriate. 2023 Attendance (so far) Spring Fling = 466 Mountain Mayhem = 415 Forks & Fireworks = 544 FreeFalling = 505 Using the MAD metric for forecast accuracy, which forecasting method is best for this data set? a. Segregation of events with event-specific trend lines b. Decomposition

Scenario: SkyRim Bike Park holds five major events each year. Park capacity is limited to 600 guests per event. As part of their long-term expansion planning, the park's management team has requested a forecast to determine when existing park capacity will be exceeded. You have been provided attendance data for each event over the past six years. Part 1: Forecasting by Trendlines Create five separate scatter plots showing each event's attendance history and create a forecast for each event using trendlines. Report the event and year in which your forecast predicts park capacity will first be exceeded. Part 2: Forecasting with Decomposition Your team of analysts has calculated a seasonal index for each event. Use the decomposition method to forecast the event and year in which park capacity will first be exceeded. Part 3: Forecast Accuracy Suppose that four of the year's five events are complete and you have actual attendance data for those events. Use the MAD metric to determine which of your forecasting models is most appropriate. Then, use that model to forecast Winter Wheels attendance for 2023. 2023 Attendance (so far) Spring Fling =466 Mountain Mayhem =415 Forks \& Fireworks =544 FreeFalling =505