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Terms - forecasting

# Terms - forecasting - GLOSSARY OF FORECASTING TERMS...

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GLOSSARY OF FORECASTING TERMS ADDITIVE SEASONALITY: When the seasonal effect on demand is not a function of the size of demand. For example, if demand is always 10 units lower than normal in a given period, regardless of whether the normal size of demand is 250 or 500, then additive seasonality is present. BASE (OR HORIZONTAL, OR LEVEL) COMPONENT: The part of the pattern in a time series that is always present, even when no other part of the pattern is present. It can be thought of as the average of the time series, or alternatively, as the level from which the time series (the historical demand) grows or declines. BIAS: The average forecast error, allowing positive and negative forecast errors to cancel each other out. The optimal bias is zero, although a bias of zero does not indicate perfect forecasting. A positive bias implies a tendency to underforecast, while a negative bias implies a tendency to overforecast. CAUSAL MODELS: Techniques that derive forecasts by identifying the "causes" of fluctuations in demand, such as the number of competitors, general economic indicators, population growth, advertising expenses, prices, etc. The most popular of these methods is multiple regression. It should be noted that the term "causal" is somewhat of a misnomer, since these models only identify whether demand is related to factors such as the ones listed above. They cannot actually prove that any factors cause demand to fluctuate in any way. CHANGE IN SEASONALITY: A permanent change in the seasonal (i.e., yearly) pattern in a time series. Such a change is best identified by plotting the residuals (forecast errors) that would result if your forecasting model takes into account all relevant components (i.e., base, trend, randomness) except seasonality (i.e, the forecast is unseasonalized). By closely examining the seasonal pattern that will show up in the residuals from year to year, any significant changes can be identified. To be reasonably confident that a change is "permanent," the new pattern should hold true for at least two years. The response to a change in seasonality is generally to ignore the full years of data before the change when building the forecasting model. CHANGE IN TREND: A permanent change in the general movement upward or downward in a time series. Such a change is best identified by plotting the residuals (forecast errors) that would result if your forecasting model takes into account all relevant components (i.e., base, trend, seasonality, randomness), or in other words, the forecast is trend and seasonally adjusted. A change in trend is generally identified as a relatively sharp, one-time change of direction in the residuals. If the residuals change direction more than once over a period of several years, and/or the change of direction(s) is(are) follow more of a rolling wave- like curve, then it is possible that the time series has cyclicality, not a change in trend. The response to a change in trend is generally to ignore the full years of data before the change when building the forecasting model.

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