Example: Method 5: Linear Approximation

Linear Approximation calculates a trend that is based upon two sales history data points. Those two points define a straight trend line that is projected into the future. Use this method with caution because long range forecasts are leveraged by small changes in just two data points.

Forecast specifications: n equals the data point in sales history that is compared to the most recent data point to identify a trend. For example, specify n = 4 to use the difference between December (most recent data) and August (four periods before December) as the basis for calculating the trend.

Minimum required sales history: n plus 1 plus the number of time periods that are required for evaluating the forecast performance (periods of best fit).

This table is history used in the forecast calculation:

Past Year	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
1	None	None	None	None	None	None	None	129	131	114	119	137

Calculation of Linear Approximation, given n = 4

(137 - 129)/4 = 2.0

This table is the Linear Approximation forecast for next year, given n = 4:

Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
139	141	143	145	147	149	151	153	155	157	159	161

January forecast = December of past year 1 + (Trend) which equals 137 + (1 × 2) = 139.

February forecast = December of past year 1 + (Trend) which equals 137 + (2 × 2) = 141.

March forecast = December of past year 1 + (Trend) which equals 137 + (3 × 2) = 143.