This article describes forecasting methods, their meaning, classification and brief characteristics. The main criteria for choosing these methods are presented and examples of their effective practical application are given. The special role of forecasting methodology in the modern world of increased instability was also emphasized.
The essence and significance of forecasting methodology
In the general concept, forecasting is a process of predetermining the future based on initial parameters (experience, identified patterns, trends, connections, possible prospects, etc.). On a scientific basis, forecasting is used in various areas of human life: economics, sociology, demography, political science, meteorology, genetics, and many others. The most illustrative example of the use of forecasting in a person's daily life is the daily weather forecast familiar to everyone.
In turn, the effective use of forecasts on a scientific basisrequires the use of certain techniques, including a number of forecasting methods. At the beginning of the last century, at the beginning of scientific research in this area, only a few similar methods with a limited range of applications were proposed. At the moment, there are many such methods (more than 150), although practically no more than a few dozen basic forecasting methods are used. At the same time, the choice of certain methods depends both on the scope of their application and on the goals of the ongoing predictive research, as well as on the availability of specific forecasting tools for the researcher.
Basic concepts in forecasting methodology
Forecasting method - a specific method aimed at studying the object of forecasting in order to obtain a target forecast.
Forecasting methodology - the total body of knowledge about the methods, techniques and tools for making forecasts.
Forecasting technique - a combination of methods, techniques and tools chosen to obtain a target forecast.
Forecasting object - a certain area of processes within which research is carried out on the subject of forecasting.
The subject of forecasting is a legal or natural person carrying out research work in order to obtain forecasts.
Differences and relationship between planning and forecasting process
Forecasting versus planning:
- is informative, not directivecharacter;
- covers not only the activities of a particular enterprise or organization, but the entire set of external and internal environment;
- may be longer term;
- does not require much detail.
However, for all the differences, forecasting and planning are closely related, especially in the economic field. The resulting target forecast shows the area of potential risks and opportunities, in the context of which specific problems, tasks and goals are formed that need to be solved and taken into account when drawing up plans of various forms (strategic, operational, etc.). In addition, forecasts provide an analytically sound multivariate view of potential development, which is necessary for the construction of alternative plans. In a general sense, we can say that the relationship between forecasting and planning lies in the fact that although the forecast does not define specific planned tasks, it contains the necessary informative materials for effective target planning.
Main classifiers in forecasting methodology
The main classification of forecasting methods is usually carried out according to the following features:
By degree of formalization:
- intuitive (heuristic) methods that are used in difficult-to-predict tasks using expert assessments (interviews, scenario method, Delphi method, brainstorming, etc.);
- formalized methods that are predominantlyimply more accurate mathematical calculation (extrapolation method, least squares method, etc., as well as various modeling methods).
By the nature of the forecasting process:
- qualitative methods based on expert assessments and analytics;
- quantitative methods based on mathematical methods;
- combined methods, including (synthesizing) elements of both qualitative and quantitative methods.
According to the method of obtaining and processing information data:
- statistical methods, implying the use of quantitative (dynamic) structural patterns for processing information data;
- methods of analogies based on logical conclusions about the similarity of patterns of development of various processes;
- leading methods, characterized by the ability to build forecasts based on the latest trends and patterns of development of the object under study.
Also, the totality of these methods can be conditionally divided into general forecasting methods and specialized methods. General methods include those that cover a wide range of solving prognostic problems in various spheres of life. An example of such forecasts can serve as expert assessments in various fields. On the other hand, there are methods focused only on a certain area of activity, such as the balance method, which has become widespread in the economic sphere and is focused on accounting information.
Brief description of forecasting methods
As already noted, there are many methods in forecasting at the moment. The main forecasting methods include those that are currently most widely used and used in various fields.
- Method of expert estimates. Since, when solving many forecasting problems, there are often insufficiently reliable formalized, including mathematical, data, this method is quite popular. It is based on the professional opinion of experienced experts and specialists in various fields, followed by the processing and analysis of surveys.
- The extrapolation method is used with stable systemic dynamics of various processes, when development trends persist in the long term and there is a possibility of projecting them onto future results. Also, this method is used for objects of the same field of activity with similar parameters, assuming that the impact of certain processes on one object that caused certain consequences will cause similar results in other similar objects. Such forecasting is also called the analogy method.
- Modeling methods. The development of models is carried out on the basis of an assessment of data on certain objects or systems, their elements and processes, followed by experimental testing of the constructed model and making the necessary adjustments to it. At the moment, predictive modeling methods have the widest range of applications in variousareas from biology to the socio-economic sphere. In particular, the possibilities of this technique have been revealed with the advent of modern computer technology.
- The normative method is also one of the main methods. It implies an approach to making forecasts focused on specific goals and objectives, formulated by the subject of forecasting with the installation of certain standard values.
- The scenario method has become widespread in the development of management decisions that allow assessing the probabilistic development of events and possible results. That is, this method involves an analysis of the situation with the subsequent determination of probable trends in its development under the influence of the adoption of certain management decisions.
- Foresight methods. The latest methodology, which includes a whole range of different methods and techniques aimed not only at analyzing and forecasting the future, but also at its formation.
Statistical forecasting methods
One of the main methods of making forecasts are statistical methods. The forecasts developed by such methods can be the most accurate, provided that the initial information data are complete and reliable for the analysis of the necessary quantitative and semi-quantitative characteristics of forecasting objects. These methods are a form of mathematical forecasting techniques that make it possible to build promising time series. Statistical forecasting methods include:
- research and application of modern mathematicsstatistical methodology for constructing forecasts based on objective data;
- theoretical and practical research in the field of probabilistic-statistical modeling of expert forecasting methods;
- theoretical and practical research of forecasting in a risky environment, as well as combined methods of symbiosis of economic-mathematical and econometric (including formalized and expert) models.
Auxiliary tools for forecasting methodology
Auxiliary tools of heuristic forecasting methods include: questionnaires, maps, questionnaires, various graphic material, etc.
The tools of formalized and mixed methods include a wide range of tools and techniques of auxiliary mathematical apparatus. Specifically:
- linear and non-linear functions;
- differential functions;
- statistical and mathematical tools for correlation and regression;
- least squares;
- matrix techniques, apparatus of neural and analytical networks;
- apparatus of the multidimensional central limit theorem of probability theory;
- apparatus of fuzzy sets, etc.
Criteria and factors for choosing certain methods when making forecasts
Various factors influence the choice of forecasting methods. So operational tasks require more operational methods. At the same time, long-term (strategic forecasts) require the use of forecasting methodscomprehensive and comprehensive. The choice of certain methods also depends on the scope, availability of relevant information, the possibility of obtaining formalized (quantitative) estimates, the qualifications and technical equipment of forecasting subjects, etc.
The main criteria of the methodology can be:
- systemic nature in the formation of forecasts;
- adaptability (variability) to possible parametric changes;
- validity of the choice of methodology in terms of reliability and relative accuracy of the forecast;
- continuity of the forecasting process (unless a one-time task is set);
- economic feasibility - the cost of implementing the forecasting process should not exceed the effect of the practical application of its results, especially in the economic sphere.
Examples of the effective application of the existing forecasting apparatus
Effective practical application of forecasting methods, an example of which is the most common at the moment, is their use in a business environment. So the most progressive firms can no longer do without making forecasts in the implementation of full-fledged planning of their activities. In this context, forecasts of market conditions, price dynamics, demand, innovative prospects and other predictive indicators up to seasonal and climatic natural fluctuations and socio-political climate are important.
Besides this, there are manyexamples of effective application of forecasting methodology in various spheres of human life:
- use of mathematical modeling to predict potential emergencies at hazardous enterprises;
- systemic environmental and economic forecasting by country and regions;
- socio-economic forecasting of trends in the development of society as a whole and its individual elements;
- prediction in quantum physics, new biotechnology, information technology and many other fields.
The role of forecasting methodology in today's world of increased uncertainty and global risks
In conclusion, it must be said that the forecasting methodology has long been fully integrated into human life, but it is becoming most relevant today. This trend is associated both with the rapid development of technological processes in the world, and with an increase in uncertainty in the internal and external environment. Numerous crisis phenomena in the economy, politics, social sphere provoke an increase in the risk load in all areas of activity. The deepening of globalization processes has led to the emergence of systemic global risks generating a possible domino effect, when problems in individual corporations or countries have a serious negative impact on the economic and political state of the entire world community. Also, the risks associated with natural and climatic instability, major man-made disasters, militarypolitical crises. All this testifies to the special role of forecasting both potential global and current individual risk phenomena in the modern world. Effective systemic forecasting that meets today's challenges can avoid or reduce the consequences of many threats and even transform them into benefits.
