Additive Season, Linear Trend

In this Time Series model, the simple exponential smoothing forecasts are "enhanced" both by a linear trend component (independently smoothed with parameter ) and an additive seasonal component (smoothed with parameter ). For example, suppose we were to predict the monthly budget for snow-removal in a community. There may be a trend component (as the community grows, there is a steady upward trend for the cost of snow removal from year to year). At the same time, there is obviously a seasonal component, reflecting the differential likelihood of snow during different months of the year. This seasonal component could be additive, meaning that a particular fixed additional amount of money is necessary during the winter months, or (see below) multiplicative, that is, given the respective budget figure, it may increase by a factor of, for example, 1.4 during particular winter months.
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