Forecasting can be defined as Estimating or predicting future events or conditions. Forecasts may be long-term or short-term. The techniques used may be quantitative (often making sue of computers) or qualitative. Quantitative forecasting models may be classified into (a) causal models in which independent variables are used to forecast dependent variables, and (b) time series models, which produce forecasts by extrapolating the historical values of the variables of interest by, e. g., moving averages.
Seasonal Model Seasonality is a pattern that repeats for each period. For example annual seasonal pattern has a cycle that is 12 periods long, if the periods are months, or 4 periods long if the periods are quarters. The seasonal index is required to be found for each month, or other periods, such as quarter, week depending on the data availability (Hossein, 1994-2006). Seasonal Index: Seasonal index represents the extent of seasonal influence for a particular segment of the year.
The calculation involves a comparison of the expected values of that period to the grand mean. A seasonal index is how much the average for that particular period tends to be above (or below) the grand average. Therefore, to get an accurate estimate for the seasonal index, compute the average of the first period of the cycle, and the second period and divide each by the overall average (Hossein, 1994-2006). A seasonal index of one for a particular month indicates that the expected value of that month is 1/12 of the overall average. A seasonal index of 1.
25 indicates that the expected value for that month is 25% greater than 1/12 of the overall average. A seasonal index of 80 indicates that the expected value for that month is 20% less than 1/12 of the overall average (Hossein, 1994-2006). Deseasonalizing Process Deseasonalizing the data, also called Seasonal Adjustment is the process of removing recurrent and periodic variations over a short time frame, e. g. , weeks, quarters, months. Therefore, seasonal variations are regularly repeating movements in series values that can be tied to recurring events.
The Deseasonalized data is obtained by simply dividing each time series observation by the corresponding seasonal index (Hossein, 1994-2006). Almost all time series published by the US government are already deseasonalized using the seasonal index to unmasking the underlying trends in the data, which could have been caused by the seasonality factor (Hossein, 1994-2006). Forecasting 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 (Hossein, 1994-2006). Technological Forecasting Model Technological forecasting is a subset of futures research. Futures research is an umbrella term which encompasses “any activity that improves understanding about the future consequences of present developments and choices” (Amara and Salanik, 1972, p. 415). In defining forecasting, the authors offer the following progression.
Technological forecasting includes “all efforts to project technological capabilities and to predict the invention and spread of technological innovations” (Ascher, 1979, p. 165). Martino (1983) states that a technological forecast includes four elements: the time of the forecast or the future date when the forecast is to be realized, the technology being forecast, the characteristics of the technology or the functional capabilities of the technology, and a statement about probability (Donnelly, n.d. ).
Forecasting a technology is a difficult task and includes “the uncertainty and unreliability of data, the complexity of ‘real world’ feedback interactions, the temptation of wishful or emotional thinking, the fatal attraction of ideology, [and] the dangers of forcing soft and somewhat pliable ‘facts’ into a preconceived pattern” (Ayers, 1969, p. 18). These methods also rely on judgment and are particularly appropriate for very new technologies and very long-range forecasting.