There are many forecasting methods including seasonal, Delphi, technological and time series. Depending upon the situation, one may work better for a company than another. In describing forecasting, Amara and Salanik (1972) offer the following: Forecasting is:
a statement about the future:,
a probabilistic statement about the future:
a probabilistic, reasonably definite statement about the future: a probabilistic, reasonably definite statement about the future, based upon an evaluation of alternative possibilities. (p. 415)
All forecasts are made under some conditions of uncertainty, as the future is never entirely predictable. Seasonal Forecasting Models
Companies may experience demand fluctuations by season. Seasonality is the pattern that repeats for each period or season. Seasonal forecasting is a decomposition of a times series forecast model. Data is gathered for each season to gain a picture of the seasonal factor for the period. Using the simple proportion, the seasonality factor consists of a comparison of the period or season average and the overall average. This allows the company to determine if sales are expected to be above or below average during certain times or seasons of the year. A seasonal model can be as simple or complicated as needed. The seasonal factor can be computed using a simple proportion, a hand-fit straight line or be decomposed using least squares regression. Incorporating seasonality in a forecast is useful when the time series has both trend and seasonal components. The final step in the forecast is to use the seasonal index to adjust the trend projection. One simple way to forecast using a seasonal adjustment is to use a seasonal factor in combination with an appropriate underlying trend of total value of cycles (Arsham, 1994).
Seasonality charts are most accurate during periods with stable market conditions. Also, data from more than one season is best gathered in order to illustrate...