ForecastingHandouts (1) - Forecasting Methods BUAD351 Time...

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Forecasting Methods BUAD351 Time Series Data Given time series data, always graph it to see what forecast method might be most appropriate. Naïve Methods A naïve forecast assumes that the current period’s actual sales = next period’s forecasted demand. Naïve methods are used for products with relatively stable demand . A naïve method could be used to predict demand where trend is relatively steady by adding a percentage of the prior period’s demand to account for growth. Naïve methods can be used to account for seasonality (i.e., assume the prior seasonal variation will repeat). Averaging Methods Averaging methods are used for relatively stable demand that varies around an average , for step changes in demand, or for gradual increases or decreases . The purpose of averaging is to smooth out forecasted demand since we know that a portion of demand variability is random and we don’t want to adjust production to random variability. Moving Average (using n periods of historical data) Choose a number (n) of periods over which to smooth demand. Larger values of n create a smoother forecast. Given n periods: Moving Average = (Sum of Actual Demand over n prior periods)/n Given historical data, the Moving Average forecast will start in the n + 1 period. As you forecast future periods, the earliest historical period is dropped from the average and the most recent actual is added. Weighted Moving Average (using n periods of historical data) The weighted moving average applies weights (which sum to 1) to a number of prior periods. Each weight is multiplied by the corresponding period of historical actual demand. The highest weight is applied to the most recent period. For example, given: Weights of .5, .3, and .2 Period 1 Actual Demand = A 1 Period 2 Actual Demand = A 2 Period 3 Actual Demand = A 3 Weighted moving average = .5(A 3 ) + .3(A 2 ) + .2(A 1 ). Exponential Smoothing Exponential smoothing takes a portion of the prior period’s error (difference between Actual Demand and the Forecast) and adds it to the prior period’s forecast. The portion of the error applied is defined by the smoothing constant α .
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