The information is then collected and summarized and presented to the experts. The experts can then reconsider their answers and adjust them. This process can continue as required, with the intention being for a general consensus to emerge. The purpose of the technique is to utilize a range of experts, but in a way where each gives their opinion independently. The main difference between this method and other forecasting methods is that the forecasting is based on opinions, rather than data. Another forecasting technique is moving-average forecasting. It is used to predict future events based on the assumption that future events will be based on past events. Another related method based on the same assumption is exponential smoothing. This method takes the same approach as moving-average forecasting and also forecasts future events based on past events. The difference is that the calculation includes an adjustment that takes into account both the data of the previous period and the data predicted for the previous period. This creates greater accuracy. Both these methods use relatively simple formulas that uses past data to predict future data. This means that the methods are only useful where there is past data to base the predictions on and where past data is considered a valid predictor of the future. For example, if a company's time-series analysis shows continuous spikes and variations and no clear general trends, then the data of one period may not be a good predictor of future periods.

Another forecasting...

Regression models are defined as "statistical techniques used to describe the relationship between the variable being forecast and other variables" (Slack, Chambers, Harland, Harrison & Johnston 1998, p. 829). For example, consider a company where sales is the variable being forecast and sales is considered to be dependent on the strength of the economy. By plotting past data on sales against past data on the strength of the economy, the trend would be seen. By knowing the current and future strength of the economy, the company could then forecast demand. In real situations, regression analysis uses more complicated statistics because there is more than one factor influencing the variable being forecast. Whether or not regression analysis is useful depends on whether there are clear trends between variables. Regression analysis can also be made ineffective if there are so many factors influencing a variable that the statistics becomes too complicated.
This shows that there are various types of forecasting methods and describes how they all differ. The choice of method to use depends on the data available, the situation, and the link between variables and the factors impacting on the variables.