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 Forecasting Guide Overview Categories 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 Regression Leading Indicator Input-Output Models
Time Series Models

OVERVIEW: These techniques extrapolate past data into the future. The premise is that there is some underlying pattern in the value of the variable being forecast.

GENERAL APPROACH: To choose the appropriate technique, first do the following:

STEP 1: Date Examination - Graphical Analysis

• What is the pattern in the data?

• Try plotting the historical data on a chart

• Look for a trend line by fitting a simple line or curvilinear trend curve to the data in a freehand method.

• Look for cyclical patterns, if any, and/or major turning points

• Choose an appropriate technique. Table A2 presents different techniques and their capabilities in recognizing various patterns. For example,

• If the trend line is horizontal or reasonably stable (little or no slope), try simple versions of:

• Moving averages, or

• Exponential smoothing

If slope is evident, try

• Double moving averages, or

• Double exponential smoothing

Fluctuating patterns can be handled with:

• Decompositional models, or more complex approaches such as:

• Box-Jenkins models

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