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Thedecomposition technique is the process of identifying and separating time series data into fundamental components like trend and seasonality. When demand contains both seasonal and trend effects at an identical time, the question is how they relate to each other. In this description, we examine two varieties of seasonal variation:

Additive and Multiplicative.

Additive seasonal variation simply assumes that the seasonal amount may be a constant regardless of the trend or average amount.

$\text{Forecastincludingtrendandseasonal=Trend+Seasonal}$

In aMultiplicative seasonal variation, the trend is multiplied by the seasonal factors.

$\text{¹ó´Ç°ù±ð³¦²¹²õ³Ù¾±²Ô³¦±ô³Ü»å¾±²Ô²µ³Ù°ù±ð²Ô»å²¹²Ô»å²õ±ð²¹²õ´Ç²Ô²¹±ô=°Õ°ù±ð²Ô»å׳§±ð²¹²õ´Ç²Ô²¹±ô´Ú²¹³¦³Ù´Ç°ù}$

a = 4210.25 and b = -168.24

The formula given below is used to calculate the forecast including trend and seasonal factors for the next quarter.

Forecast including trend and seasonal = Trend x Seasonal factor

Theforecast demand for the next four quarters is shown in column (4) of the table given above.