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Unformatted text preview: responses to new product medium term (3 months -- 2 years) for planning strategy for meeting demand for next 6 months to 1 years; captures seasonal effects long term (> 2 years) for detecting general trends and identifying major turning points (pg. 314-315) Decomposition of a Time Series: separating times series data into components easy to identify trends (plot and see direction of movement) & seasonal components (compare same period year to year) hard to identify cycles (may be months or years long) seasonal variations: additive (trend + seasonal) and multiplicative (trend x seasonal factor; amount of correction needed in a time series to adjust for the season of the year) Decompose time series into its components a. Find seasonal component b. Deseasonalize the demand c. Find trend component . Forecast future values of each component a. Project trend component into the future b. Multiply trend component by seasonal component...
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- Fall '11