Time series decomposition is the breaking down of given data to come up with a given relationship that can predict the future. The various characteristics (trend, seasonal variation, cyclical variation and residual variation) are separated. The two ways of forecasting are by use of multiplicative and additive models.
In multiplicative model the steps involved include the following
- Deseasonalise the data by separating of trend and seasonal variation (using moving averages)
- Calculate the trend using least squares method
- Estimate the dependent variables from the regression formula
- Calculate the percentage variation of the dependent variable in each period
- Average this percentage variation of dependent variable to give average seasonal variation
- Forecast is made by multiplying trend with the percentage variation
Wilfykil answered the question on February 20, 2019 at 10:33
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