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    Forecast and Accuracy Measures

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    Treat each day of week as a different time series. For example, since this flight departs seven days per week, create seven forecasts. Sort the data by day of week and date. Then create scatter plots or line plots of each time series. From the plots, determine what the components of each time series are.

    Next, using the MSE, MAD, MAPE, LAD, select the best model for each of the seven days using root mean squared error (RMSE). You will end up with seven different models to make seven different forecasts. Last, use these models to forecast no shows for each of the days next week (the week beginning on Monday, November 22)

    [Please refer to the EXCEL sheet for data]

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    This solution is comprised of detailed step-by-step calculations and explanation of Forecast Techniques and various Forecast Accuracy Measures like Forecast Error, Absolute Error, MAPD etc. in EXCEL. Comparison of these Forecast Techniques based on these Forecast Accuracy measures has also been shown. Forecast and accuracy measures are examined.

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