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Topic 2 DQ 2 BUS 660 DQ 2: Explain how error measures can help to optimize moving averages, weighted moving averages, and exponential smoothing methods. Answer: Forecast precision is tested by equations used to identify inaccuracies in projection calculations, also known as forecast errors (Render, Stair, Hanna, & Hale, 2015, pg. 206). Render, Stair, Hanna, and Hale (2015) list the different prediction assessors as the mean forecast error also known as MFE, mean absolute error (MAE), and mean squared error or MSE abbreviated (p. 207-208). Obtaining the means for these errors computes the accuracy of the anticipation procedures as they pertain to pass forecasting data. There are three different forecasting methods; moving averages, weighted moving averages, and exponential smoothing, all of which are
fundamentally used in sequences of planar nature to negotiate arbitrary data points (Render, Stair, Hanna, & Hale, 2015, pg. 211). The moving averages approach utilizes contemporary statistics (typically three or more) within a specific period to create a forecast for the next cycle. In relation to the moving averages approach MSE, MFE, and MAE aid in providing more accuracy for the next period’s forecast because the data is reliant upon several previous forecasts instead of just one. This also beneficial because the forecast projection can change overtime, as more data is collected the forecasts are updated. The weighted moving averages are similar to the moving averages calculation, however, data points taken at different times are weighed differently. Typically, the most recent forecasts carry more weight and the older statistics measureless in the calculation as a whole. Under the assumption that the data used for the old time periods will apply to the next forecast, it is recommended to use the MSE forecast calculation to measure accuracy. Exponential smoothing has similarities to both moving averages and weighted moving averages in that the data with the most weight is the most recent forecast. A variable called the smoothing constant is added as a standard of all of the former figures from a specific period (Render, Stair, Hanna, & Hale,2015, pg. 215). The MSE calculation is useful in this method as well because larger smoothing constants allow for rapid alterations to the forecasts, decreasing MSE. References: Render, B., Stair, R. M., Jr., Hanna, M. E., & Hale, T. S. (2015). Quantitative analysis for management(12thed.). Boston, MA: Pearson Education, Inc. Milton, A. (2018). Simple, exponential and weighted moving averages. Retrieved from Proverbs 16:9 NIV “In their hearts humans plan their course, but the Lord establishes their steps.” Often we plan so much of our lives out to prepare and anticipate what’s going to happen next, but sometimes our plans don’t pan out the way we’d like. What we plan for ourselves is nowhere near as fruitful as God’s plan.
