T18-02 - 1 T18-02 Weighted Moving Average Forecast Purpose Allows the analyst to create and analyze the "Weighted Moving Average" forecast for up to 5.

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T T18-02 Weighted Moving Average Forecast Purpose Allows the analyst to create and analyze the "Weighted Moving Average" forecast for up to 5 weighted moving average periods. The MAD and MSE for the forecast are calculated, and a graphical representation of history and forecast are shown. Inputs Historical Period Demand Periods (n) for Moving Average Forecast n weights Outputs Weighted Moving Average Forecast Forecast Error MAD & MSE Graph showing Historical Demand and Weighted Moving Average Forecast Limitations 60 Time Series Observations n = 1, 2, 3, 4, or 5

T Quantitative Methodologies Quantitative methods model historical demand variation patterns (random, trend, seasonal or cyclical). Once past history has been explained by a model, extrapolations can be made about the future. Some simplistic techniques are Averaging/Smoothing Models. Naïve. Moving average. Weighted moving average. Exponential smoothing These techniques are best used when demand is stable with no trend or seasonal pattern Stable

T Averaging/Smoothing Models Weighted moving average of n periods – next period forecast is the weighted average of the previous n periods actual demand Horizon: Short range Method: Strength: Same as moving average, weights determine influence of previous periods demand Weakness: Not very accurate when a longer forecasting horizon is necessary, lags actual demand

T Given that a forecast is rarely correct, the methodology you choose should be the one which provides the least error from the actual historical demand. Forecast error is defined as the difference between actual historical demand and the forecast. Forecast Accuracy

T Forecast Error

T There are two measures used to monitor the accuracy of a forecast. The Mean Absolute Deviation (MAD) and the Mean Squared Error (MSE). The MAD is the average of the absolute value of the forecast errors. The MSE is the average of the squared forecast errors. Note : The formula for the MSE shown above may vary slightly. Some textbooks divide the sum of the squared errors by n-1 rather than n. Monitoring the Forecast

T Weighted Moving Average Example Prepare a 3 period weighted moving average forecast for the historical demand data shown below. Use weights of.5 for the most recent period,.3 for the second most recent period, and.2 for the third most recent period. Calculate the 3 weighted period moving average forecast, MAD and MSE.

T Input the History Values, Number Periods for the Weighted Moving Average Forecast (n), and n weights in the light green cells. The n Period Moving Average Forecast, Error, MAD, and MSE are automatically calculated.

T A graph showing the History Values and n Period Weighted Moving Forecast is automatically produced.