Telecommunications service providers perform forecasting calculations to assist them in planning their networks. Accurate forecasting helps operators to make key investment decisions relating to product development and introduction, advertising, pricing etc., well in advance of product launch, which helps to ensure that the company will make a profit on a new venture and that capital is invested wisely.
Why is forecasting used? Forecasting can be conducted for many purposes, so it is important that the reason for performing the calculation is clearly defined and understood. Some common reasons for forecasting include:
Planning and Budgeting – Using forecast data can help network planners decide how much equipment to purchase and where to place it to ensure optimum management of traffic loads. Evaluation – Forecasting can help management decide if decisions that have been made will be to the advantage or detriment of the company. Verification – As new forecast data becomes available it is necessary to check whether new forecasts confirm the outcomes predicted by the old forecasts. Knowing the purpose of the forecast will help to answer additional questions such as the following:
What is being forecast? – events, trends, variables, technology Level of focus – focus on a single product or a whole line, focus on a single company or the entire industry How often is forecasting conducted? – daily, weekly, monthly, annually Do the methods used reflect the decisions needed to be taken by management? What are the resources available to make decisions? – lead-time, staff, relevant data, budget, etc. What are the types of errors that could occur and what will they cost the company?
Factors influencing forecasting When forecasting it is important to understand which factors may influence the calculation, and to what extent. A list of some common factors can be seen below:
Technology subscriber access – fibre, wireless, wired, cellular, TDMA, CDMA, handsets application – telephony, PBXs, ISDN, videoconferencing, LANs, teleconferencing, internetworking, WANs technology – broadband, narrowband, carriers, fibre to the curb, DSL Economics Global Economics – Economic climate, predictions, estimates, economic factors, interest rates, prime rate, growth, management's outlook, investors' confidence, politics Sectoral Economics – trends in industry, investors’ outlook, telecommunications, emerging technologies growth rate, recessions, and slowdowns Macroeconomics – inflation, GDP, exports, monetary exchange rates, imports, government deficit, economic health Demographics Measurement of number of people in regions – how many were born, are living and died within a time period The way people live – health, fertility, marriage rates, ageing rate, conception, mortality
Data preparation Before forecasting is performed, the data being used must be "prepared". If the data contains errors, then the forecast result will be equally flawed. It is therefore vital that all anomalous data be removed. Such a procedure is known as data "scrubbing". Scrubbing data involved removing data points known as "outliers". Outliers are data that lie outside the normal pattern. They are usually caused by anomalous and often unique events and so are unlikely to recur. Removing outliers improves data integrity and increases the accuracy of the forecast.
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:
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:
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 curves, 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. 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. 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. The results of the analysis should then be checked using alternative forecasting methods and the results can be published. 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.
Time series methods Time series methods are based on measurements taken of events on a periodic basis. 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:
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