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Forecasting Guide
Selecting the Approach
Combining Approaches
Judgmental Models
Delphi Method
Curve Fitting
Analogous Data
Time Series Models
Moving Average
Exponential Smoothing
Decomposition Models
Box-Jenkins Models
Leading Indicator
Input-Output Models
TECHNIQUE #5: Moving Average

BASIC IDEA: Data from a number of consecutive past periods can be combined to provide a reasonable forecast for next period. The greater the number of previous periods used, the more "smoothing" takes place.

PROCEDURE: To forecast next period's sales using a simple moving average, use the following procedure: Compute,

Yt+1 = St + St-1 + St-2 + .....+ St-J


Yt+1 = Forecasted sales for next period

St = Current period sales

St-1 = Actual sales of previous period

St-2 = Actual sales two periods ago

St-J= Actual sales J periods ago

N = Number of time periods included

• Note that this forecast is simply an average of past values

Important considerations:

1. How many periods should be used?

• If overall pattern is one of regular growth or decline with few fluctuations, smaller number is usually better.

• If data have been very stable but interrupted by frequent random variations, larger number is best.

• In short, try various numbers and choose one that tends to give the best results (i.e. least forecast error).

2. If a trend is evident in the raw data, try a double moving average model to capture it:

• This is merely the moving average of a moving average! To calculate (using 3 periods), use the following formulas and plug in the appropriate values.

Yt' = St + St-1 + St-2


Yt'' = Y't + Y't-1 + Y't-2

then the forecast for next period is calculated as:

Yt+1'' = 3Y't - 2Y''t



• Fairly simple method

• Slow adjustment to changes in data pattern

• Assigns equal weight to each past observation


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