Forecasting Case Study: Urban Planning Important Note: Students must access the “County Business Patterns” Topic Material for this assignment. Students will use this U.S. Census Industry data portal to access data for a zip code with which you are familiar. This can be the zip code of your personal residence, location of employer (corporate, regional, or local office), undergraduate educational institution, hometown, etc. Scenario You have recently been hired as an urban planner for your local government. You have been tasked with determining economic growth and decline patterns in your area. As an urban planner, you will ultimately be responsible for determining patterns and forecasting for all industries within your area. However, the city manager has asked that you prioritize one of the 10 industries below. Business proposals have been submitted and the city manager would like to have use forecasting data to make an informed decision about whether to approve industry expansion or to allocate resources elsewhere. Industries • Construction • Manufacturing • Transportation and Warehousing • Information • Finance and Insurance • Real Estate and Rental and Leasing • Professional, Scientific, and Technical Services • Management of Companies and Enterprises • Administrative and Support and Waste Management and Remediation Services • Health Care and Social Assistance Forecasting Access the “County Business Patterns” page on the United States Census Bureau website and enter your city’s zip code. Using one of the industries above, access the data for the last 5 years that are available on website. Determine patterns of economic growth or decline during this time period, and develop the most optimal forecasting model for the next 2 years. Note that you will need to set up these two forecast calculations. Clearly justify why your selected model is the best one. Specifically explain what forecast error is and how you used it to ascertain the most optimal forecasting model. Assume that you are presenting your findings to senior management and that senior management has minimal knowledge of forecasting techniques and how forecast error is calculated. Forecasting Num pds Data Period Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Moving averages - 2 period moving average 3 Demand Forecasts and Error Analysis Forecast Error Absolute #DIV/0! #DIV/0! #DIV/0! Total Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! Bias #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! MAD Squared Abs Pct Err #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! MSE #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! MAPE Forecasting 1 0.9 0.8 Value 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 4 Time Demand Forecast 5 Forecasting Num pds Data Period Period 1 Period 2 Period 3 Period 4 Period 5 Period 6 Period 7 Moving averages - 3 period moving average 3 Demand Forecasts and Error Analysis Forecast Error Absolute #DIV/0! #DIV/0! Total Average #DIV/0! #DIV/0! #DIV/0! #DIV/0! Bias #DIV/0! #DIV/0! #DIV/0! #DIV/0! MAD Squared Abs Pct Err #DIV/0! #DIV/0! #DIV/0! #DIV/0! MSE #DIV/0! #DIV/0! #DIV/0! #DIV/0! MAPE Forecasting 1 0.9 0.8 Value 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 4 Time Demand Forecast 5 Forecasting Alpha Data Period Period 1 Period 2 Period 3 Period 4 Period 5 Demand Exponential smoothing Forecasts and Error Analysis Forecast Error Absolute Squared Abs Pct Err 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! 0 0 0 0 #DIV/0! Total 0 0 0 #DIV/0! Average 0 0 0 #DIV/0! Bias MAD MSE MAPE SE 0 1 0.9 Period 6 Period 7 0.8 Value 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 Forecasting 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 Time 0 4 Forecasting Alpha Beta Data Period Demand Period 1 Period 2 Period 3 Period 4 Period 5 Next period Period 6 Period 7 Trend adjusted exponential smoothing Forecasts and Error Analysis Forecast Smoothed Including Forecast, Smoothed Trend, Error Ft Trend, Tt FITt 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Total Average Bias Absolute Squared Abs Pct Err 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 #DIV/0! #DIV/0! #DIV/0! #DIV/0! #DIV/0! 0 0 0 0 0 0 #DIV/0! #DIV/0! MAPE MAD SE MSE 0 Forecasting 1 0.9 0.8 0.7 Value 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 4 Time Demand Smoothed Forecast, Ft 5