Calculation of main quantitative methods of forecasting

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1. Simple Moving Average is the most plain and simple calculation of the three main quantitative methods of forecasting. Simple Moving Average also shows a true average over time that reveals any weird changes. A disadvantage of the Simple Moving Average method is that typically it falls behind or lingers the actual data.

Weighted Moving Average is quite more scientific than the Simple Moving Average method, because it assigns a weighted factor to each value in the data set based on its age. The more recent the data, the more weight it is assigned. The Weighted Moving Average Method also tends to pick up trends faster than the Simple Moving Average Method. The main disadvantage of the Weighted Moving Average Method is that it tends to fall behind the actual data, so when the trend changes it doesn't show it right away.

Exponential Smoothing is the most up to date method, of the three quantitative methods of forecasting, and just like the Simple Moving Average method, it is fairly easy to calculate. As far as the disadvantages of the Exponential Smoothing method go, it also tends to fall behind the data trends, and seasonality is typically ignored when using the double exponential smoothing method.

2. Measures of forecast error such as the Mean Absolute Deviation method and the Mean Squared Error method are utilized to determine which forecasting method is the most appropriate. Mean Absolute Deviation is used in order to calculate the average absolute forecast error, while the Mean Squared Error is used to calculate the average of squared forecast errors. For example, if forecasts are means of future distributions on past observations, then the Mean Squared Error method should be used. If forecasts are medians of future distributions on past observations, then the Mean Absolute Deviation method should be used. Another method of forecast error is the Mean Absolute Percentage Error, which is a variation of the Mean Absolute Deviation method because it shows the ratios of the absolute errors to the actual demand for a number of periods.

Reference no: EM132289583

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