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# ma16 - multiply it by the seasonal index for that season...

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Quantitative Forecasting Methods (Seasonal Indexing) Sometimes, there are seasonal variations in demand. For example, Gainesville bars are busier Thursday through Saturday than from Sunday through Wednesday. Likewise, ski resorts are busier in the winter, and cruise lines are busier in the summer. When there are seasonal variations in demand, we can use the multiplicative seasonal model to modify trend data such that our forecasts are better. The basic steps in this model are as follows: 1. Find the average historical demand for each season. 2. Compute the average demand over all seasons. 3. Come up with a seasonal index for each season. 4. Estimate the total demand for next year. 5. Divide this estimate by the number of seasons, then

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Unformatted text preview: multiply it by the seasonal index for that season. Note that a “season” can be any amount of time during which there is a repetitive oscillation. For example, a “season” may be a day, a week, a month, or a year. Seasonal indices can be used in conjunction with linear trend analysis via regression analysis. For example, a least-squares regression might predict demand out for 24 periods, and then seasonal indices can be computed for each period. In this fashion, least-squares projections can be adjusted to better reflect seasonal variation. The key, however, is to ensure that the periods being considered match up well with the seasons....
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