QUANTITATIVE TECHNIQUES :-
Quantitative forecasting techniques are generally more objective than their qualitative counterparts. Quantitative forecasts can be time-series forecasts (i.e., a projection of the past into the future) or forecasts based on associative models (i.e., based on one or more explanatory variables). Time-series data may have underlying behaviors that need to be identified by the forecaster. In addition, the forecast may need to identify the causes of the behavior. Some of these behaviors may be patterns or simply random variations.
Among the patterns are: Trends, which are long-term movements (up or down) in the data.
Seasonality, which produces short-term variations that are usually related to the time of year, month, or even a particular day, as witnessed by retail sales at Christmas or the spikes in banking activity on the first of the month and on Fridays.
Cycles, which are wavelike variations lasting more than a year that are usually tied to economic or political conditions.
Irregular variations that do not reflect typical behavior, such as a period of extreme weather or a union strike.
Random variations, which encompass all non-typical behaviors not accounted for by the other classifications.
QUALITATIVE TECHNIQUES :-
Qualitative forecasting techniques are generally more subjective than their quantitative counterparts. Qualitative techniques are more useful in the earlier stages of the product life cycle, when less past data exists for use in quantitative methods. Qualitative methods include the Delphi technique, Sales Force Forecast (Opinions of Sales Force members), Nominal Group Technique (NGT), Jury of Executive Opinions, Users Expectations (via surveys, questionnaires, and other tools) and market research.