Editing Telecommunications forecasting (section)

==Forecasting methods==

There are many different methods used to conduct forecasting. They can be divided into different groups based on the theories according to which they were developed:<ref name="kennedy" />

===Judgment-based methods===

Judgment-based methods rely on the opinions and knowledge of people who have considerable experience in the area that the forecast is being conducted. There are two main judgment based methods:<ref name="kennedy" />

*'''Delphi method''' – The [[Delphi method]] involves directing a series of questions to experts. The experts provide their estimates regarding future development.  The researcher summarizes the replies and sends the summary back to the experts, asking them if they wish to revise their opinions. The Delphi method is not very reliable and has only worked successfully in very rare cases.
*'''Extrapolation''' – [[Extrapolation]] is the usual method of forecasting. It is based on the assumption that future events will continue to develop along the same boundaries as previous events i.e. the past is a good predictor of the future. The researcher first acquires data about previous events and plots it. He then determines if there a pattern has emerged, and if so, he attempts to extend the pattern into the future and in so doing begins to generate a forecast of what is likely to happen. To extend patterns, researchers generally use a simple extrapolation rule, such as the S-shaped [[logistic function]] or [[Gompertz curve]]s, or the Catastrophic Curve to help them in their extrapolation. It is in deciding which rule to use that the researcher’s judgment is required.

===Survey methods===

Survey methods are based on the opinions of customers and are thus reasonably accurate if performed correctly. In performing a survey, the survey’s target group needs to be identified.<ref name="goodman">Goodman A., Surveys and Sampling, 7 November 1999 http://deakin.edu.au/~agoodman/sci101/index.html Last accessed 30 January 2005.</ref> This can be achieved by considering why the forecast is being conducted in the first place. Once the target group has been identified, a sample must be chosen. The sample is a sub-set of the target and must be chosen so that it accurately reflects everyone in the target group.<ref name="goodman" /> The survey must then pose a series of questions to the sample group and their answers must be recorded.

The recorded answers must then be analyzed using statistical and analytical methods. The average opinion and the variation about that mean are statistical analytical techniques that can be used.<ref name="goodman" /> The results of the analysis should then be checked using alternative forecasting methods and the results can be published.<ref name="goodman" /> It must be kept in mind that this method is only accurate if the sample is a balanced and accurate subset of the target group and if the sample group has accurately answered the questions.<ref name="goodman" />

===Time series methods===

[[Time series]] methods are based on measurements taken of events on a periodic basis.<ref name="kennedy" /> These methods use such data to develop models which can then be used to extrapolate into the future, thereby generating the forecast. Each model operates according to a different set of assumptions and is designed for a different purpose. Examples of Time Series Methods are:<ref name="kennedy" />

*'''Exponential smoothing''' – This method is based on a moving average of the data being analyzed, e.g. a moving average of sales figures
*'''Cyclical and seasonal trends''' – This method focuses on previous data to help define a pattern or trend that occurs in cyclic or seasonal periods. Researchers can then use current data to adjust the pattern so that it fits this period’s data, and in so doing can forecast what will happen during the remainder of the current season or cycle.
*'''Statistical models''' – Statistical models allow the researcher to develop statistical relationships between variables. These models are based on current data and by means of extrapolation, a future model can be created. Extrapolation techniques are based on standard statistical laws, thus improving the accuracy of the prediction. Statistical techniques not only produce forecasts but also quantify precision and reliability. Examples of this are the ERLANG B and C formulae, developed in 1917 by the Danish mathematician [[Agner Erlang]].

===Analogous methods===

Analogous Methods involve finding similarities between foreign events and the events that are being studied. The foreign events are usually selected at a time when they are more "mature" than current events. No foreign event will perfectly mirror current events and this must be kept in mind so that any necessary corrections can be made. By examining the foreign, more mature, set of events, the future of current events can be forecast.<ref name="kennedy" />

Analogous methods can be split up into two groups namely:<ref name="kennedy" />

*Qualitative (symbolical) models
*Quantitative (numeric) models

===Causal models===

Causal Models are the most accurate form of forecasting, and the most complex. They involve creating a complex and complete model of the events being forecast. The model must include all possible variables, and must be able to predict every possible outcome.

Causal Models are often so complex that they can only be created on computers. They are developed using data from a set of events. The model is only as accurate as the data used to develop it.<ref name="kennedy" />