Forecasting is the process of making predictions of the future based on past and present data and analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology, the terms “forecast” and “forecasting” are sometimes reserved for estimates of values at certain specific future times, while the term “prediction” is used for more general estimates, such as the number of times floods will occur over a long period.
Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible.
Forecasting methods can be classified as qualitative or quantitative. Qualitative methods generally involve the use of expert judgment to develop forecasts. Such methods are appropriate when historical data on the variable being forecast are either unavailable or not applicable. Quantitative forecasting methods can be used when
- past information about the variable being forecast is available,
- the information can be quantified,
- it is reasonable to assume that past is prologue (i.e. the pattern of the past will continue into the future).
Selecting a Forecasting Method
The following table illustrates general guidelines for selecting a forecasting method based on time span and purpose criteria.
|Time Span||Purpose||Forecasting Method|
|Long Range (3 or more years)||ü Capital Budgets ü Product Selection ü Plant Location||ü Delphi ü Expert Judgment|
|Intermediate (1 to 3 years)||ü Capacity Planning ü Sales Planning||ü Regression ü Time Series Decomposition|
|Short Range (1 year or less)||ü Sales Forecasting ü Scheduling ü Inventory Control||ü Trend Projection ü Moving Average ü Exponential Smoothing|
Please understand that these are general guidelines. You may find a company using trend projection to make reliable forecasts for product sales 3 years into the future. It should also be noted that since companies use computer software time series forecasting packages rather than hand computations, they may try several different techniques and select the technique which has the best measure of accuracy (lowest error).
There are also some general principles that should be considered when we prepare and use forecasts, which are
- Unless the method is 100% accurate, it must be simple enough so people who use it know how to use it intelligently (understand it, explain it, and replicate it).
- Every forecast should be accompanied by an estimate of the error (the measure of its accuracy).
- Long term forecasts should cover the largest possible group of items; restrict individual item forecasts to the short term.
- The most important element of any forecast scheme is that thing between the keyboard and the chair.
Steps in Forecasting
The steps in the forecasting process are as
- Identify the general need
- Select the Period (Time Horizon) of Forecast
- Select Forecast Model to be used
- Data Collection, as per indicators identified, collect data from various appropriate sources of data which is compatible le with the mod el(s) selected
- Prepare forecast
- Evaluate – Evaluate the results of the applied model, in terms of confidence interval
Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers, experts; they are appropriate when past data are not available. They are usually applied to intermediate- or long-range decisions. Examples of qualitative forecasting methods are informed opinion and judgment, the Delphi method, market research, and historical life-cycle analogy.
Quantitative forecasting models are used to forecast future data as a function of past data. They are appropriate to use when past numerical data is available and when it is reasonable to assume that some of the patterns in the data are expected to continue into the future. These methods are usually applied to short- or intermediate-range decisions. Examples of quantitative forecasting methods are last period demand, simple and weighted N-Period moving averages, simple exponential smoothing, and multiplicative seasonal indexes.
Judgmental forecasting methods incorporate intuitive judgement, opinions and subjective probability estimates. Judgmental forecasting is used in cases where there is lack of historical data or during completely new and unique market conditions.
Judgmental methods include:
- Composite forecasts
- Cooke’s method
- Delphi method
- Forecast by analogy
- Scenario building
- Statistical surveys
The Delphi method is a structured communication technique or method, originally developed as a systematic, interactive forecasting method which relies on a panel of experts.
The experts answer questionnaires in two or more rounds. After each round, a facilitator or change agent provides an anonymous summary of the experts’ forecasts from the previous round as well as the reasons they provided for their judgments. Thus, experts are encouraged to revise their earlier answers in light of the replies of other members of their panel. It is believed that during this process the range of the answers will decrease and the group will converge towards the “correct” answer. Finally, the process is stopped after a predefined stop criterion (e.g. number of rounds, achievement of consensus, stability of results) and the mean or median scores of the final rounds determine the results.
Delphi is based on the principle that forecasts (or decisions) from a structured group of individuals are more accurate than those from unstructured groups. The technique can also be adapted for use in face-to-face meetings, and is then called mini-Delphi or Estimate-Talk-Estimate (ETE). Delphi has been widely used for business forecasting and has certain advantages over another structured forecasting approach, prediction markets.
Forecast by Analogy
Forecast by analogy is a forecasting method that assumes that two different kinds of phenomena share the same model of behaviour. For example, one way to predict the sales of a new product is to choose an existing product which “looks like” the new product in terms of the expected demand pattern for sales of the product.
“Used with care, an analogy is a form of scientific model that can be used to analyze and explain the behavior of other phenomena.”
According some experts, research has shown that the careful application of analogies improves the accuracy of the forecast.
Scenario analysis is a process of analyzing possible future events by considering alternative possible outcomes (sometimes called “alternative worlds”). Thus, the scenario analysis, which is a main method of projections, does not try to show one exact picture of the future. Instead, it presents consciously several alternative future developments. Consequently, a scope of possible future outcomes is observable. Not only are the outcomes observable, also the development paths leading to the outcomes. In contrast to prognoses, the scenario analysis is not using extrapolation of the past. It does not rely on historical data and does not expect past observations to be still valid in the future. Instead, it tries to consider possible developments and turning points, which may only be connected to the past. In short, several scenarios are demonstrated in a scenario analysis to show possible future outcomes. It is useful to generate a combination of an optimistic, a pessimistic, and a most likely scenario. Although highly discussed, experience has shown that around three scenarios are most appropriate for further discussion and selection. More scenarios could make the analysis unclear.
Scenario analysis can also be used to illuminate “wild cards.” For example, analysis of the possibility of the earth being struck by a large celestial object (a meteor) suggests that whilst the probability is low, the damage inflicted is so high that the event is much more important (threatening) than the low probability (in any one year) alone would suggest. However, this possibility is usually disregarded by organizations using scenario analysis to develop a strategic plan since it has such overarching repercussions.
A field of applied statistics, survey methodology studies the sampling of individual units from a population and the associated survey data collection techniques, such as questionnaire construction and methods for improving the number and accuracy of responses to surveys.
Statistical surveys are undertaken with a view towards making statistical inferences about the population being studied, and this depends strongly on the survey questions used. Polls about public opinion, public health surveys, market research surveys, government surveys and censuses are all examples of quantitative research that use contemporary survey methodology to answer questions about a population. Although censuses do not include a “sample”, they do include other aspects of survey methodology, like questionnaires, interviewers, and nonresponse follow-up techniques. Surveys provide important information for all kinds of public information and research fields, e.g., marketing research, psychology, health professionals and sociology.
A single survey is made of at least a sample (or full population in the case of a census), a method of data collection (e.g., a questionnaire) and individual questions or items that become data that can be analyzed statistically. A single survey may focus on different types of topics such as preferences (e.g., for a presidential candidate), opinions (e.g., should abortion be legal?), behavior (smoking and alcohol use), or factual information (e.g., income), depending on its purpose. Since survey research is almost always based on a sample of the population, the success of the research is dependent on the representativeness of the sample with respect to a target population of interest to the researcher. That target population can range from the general population of a given country to specific groups of people within that country, to a membership list of a professional organization, or list of students enrolled in a school system (see also sampling (statistics) and survey sampling).