Acronyms and Variables for Forecasting
1. Dt - Actual demand in time period t. ("Period" could be week, month, quarter, etc.)
2. Ft - Forecast for period t
3. Et - Forecast error for period t; how far off the forecast turns out to be. Et
= Dt -
Ft
4. LP - Last Point Method; Future demand is forecasted to be the same as the demand from the
previous period.
5. SMA – Simple Moving Average: An average formed by calculating the average over
only a specified number (m) of consecutive periods.
6. WMA – Weighted Moving Average - Calculated by defining weight factors, W 1, W 2, …
W m, for each value in the m period moving average. If the weights are all equal, the weighted
moving average is the equivalent to a Simple Moving Average.
7. SES – Simple Exponential Smoothing - An exponentially smoothed forecast is really a
weighted moving average, with the weights getting smaller and smaller for demand that is
further in the past.
8. Level - Deseasonalized value of a time series.
9. LS – Level Smoothing: The weight of new information when calculating level.
10. Bias – A measure of forecast error. The average of the error terms.
11. MAD – Mean Absolute Deviation: A measure of forecast error. The average of
the absolute values of the error terms.
12. MSE – Mean Squared Error: A measure of forecast error. The average of the
square of the error terms.
13. SE – Standard Error: A measure of forecast error. The square root of MSE.
14. MAPE – Mean Absolute Percent Error: A measure of forecast error. The average
of the percentage error.
15. DES – Double Exponential Smoothing: SES with trend.
16. Trend - The change per period (increase or decrease) in the deseasonalized value of a time
series
17. TS – The weight of new information when calculating trend.
18. TES – Triple Exponential Smoothing: SES with trend and seasonality.
19. S - Seasonality factor.
20. SS – Seasonality Smoothing: The weight of new information when calculating
Seasonality.
21. SLR w. SI – Simple Linear Regression with Seasonality Indices: Similar to SLR, but this
forecasting model incorporates seasonality