Pure risks. Causes and risk factors. Risk at various stages of the marketing cycle. Acceptable, inevitable and unacceptable (excessive) risk. The price of risk. Risk factor. The concept of risk management in marketing. Risk management tasks. Risk forecasting. Risk insurance. Methods for reducing risk: diversification, insurance, information security, use of scientific and technological advances. The role of the human factor in risk management. Stochastic nature of a number of risks.
The construction of a command post is associated with great difficulties. If it were possible to predict the position of the CP for all planning periods over, for example, the next 10 years, then it would be possible to fairly confidently predict the economic result for this period. All stages of development of geological exploration resources, drilling, construction and operation are long-term in nature. Consequently, the totality of resources (reserves) at each of these stages is the result of previous activities, reflecting the level of demand for oil, geological, tax and economic conditions of production for the future. The state of the resource base at the present stage is characterized by the depletion of the main fields, which, in turn, affects the formation of objects and the structure of oil and gas consumption. Thus, CP values are a function of such parameters as the need for oil, the level of costs and prices, the availability and quality of open reserves. Production options reflect one or more scenarios regarding possible values of technical and economic indicators in the forecast period. The source of uncertainty arises from the impossibility of accurately assessing the quality of oil (gas) reserves, as well as reliable forecasting of prices for a long period.
The creation of information systems that ensure the collection and preparation of information contributes to solving problems of forecasting, planning and gas distribution, taking into account multivariate calculations. At this stage of development, the problems of operational regulation of reserves are also solved.
All this leaves a unique imprint on modern forecasting at all stages, starting with problem formulation and ending with specific calculations.
Interaction between an enterprise and a university should begin at the stage preceding the educational process, which significantly increases the efficiency of the activities of both participants in the interaction. Forecasting and taking into account the future needs for intellectual workers will allow the enterprise to promptly respond to industry development trends and act adequately to the current situation. The task of the university is to help the enterprise develop in accordance with the requirements of the time and market conditions, and the innovative approach we propose allows us to do this.
This publication examines the features of economic forecasting and planning of the national economy of the leading countries of the world and Russia, methodological approaches to managing the national economy and the role of individual methods in this process, stages of the evolution of market relations, features of economic models of management. Particular attention is paid to the structural-functional and integrated approach when solving the most complex problems of forecasting and planning.
Forecasting is the most important stage of the national economic management system, since any management decision is inherently the implementation of the forecast result. In this regard, the hypotheses on which forecasts are based must be clearly formulated.
At the forecasting stage, possible development goals are formed at both the national, sectoral and regional levels of management. Forecasts at the federal, industry and regional levels also take into account the results of forecast studies conducted by private organizations and corporations.
At the forecasting stage, possible development goals are formed at both the national, sectoral and regional levels of management. Forecasting is carried out by government departments at various levels, the Central Bank of the country, specialized commercial firms, private industrial, banking, insurance and trading corporations. Forecasts at the federal, industry and regional levels also take into account the results of forecasts
FORECASTING IS THE MAIN STAGE OF NATIONAL ECONOMY MANAGEMENT
At the forecasting stage, a set of forecasts is developed in a variant formulation. Forecast options may differ in intended goals
Thus, if the stages of retrospective analysis and forecasting are of a purely scientific nature, then the stage of developing a concept and policy of government bodies is predominantly political in nature, and the chosen option for the development of the country's SES is not necessarily rational according to purely economic criteria.
Due to the complexity of using a system of economic indicators and indices in forecasting, some researchers consider this approach (or method) to be more of an art than a science. Therefore, the skill and experience of the researcher, his good knowledge of the patterns of development of the economy as a whole and its features at this stage, come to the fore.
J. Tinbergen identified four stages (stages) of forecasting
However, most often problems arise in practice. If the state pursues an active structural policy and at the same time there is a clear scale of preferences regarding the sectoral structure of the economy, and the nature of the distribution of economic activity across regions is practically irrelevant, then sectoral calculations should be performed at the second stage of forecasting, regional ones at the third. In this case, regional forecasting takes into account limitations caused by decisions made at the industry level.
Let us recall that the diagram does not reflect regional forecasts, the inclusion of which would expand the forecast system and increase the number of forecast stages (stages).
At various stages of forecasting, especially when analyzing the forecast object, its classification and modeling, as well as when identifying the functions of the object, the method of analogies and associations can be used. It is designed to activate creative thinking and obtain additional information about the object under study when searching for new ideas and solutions. The method includes two types of techniques for activating creative thinking and obtaining information about the object under study, i.e. means of analysis and synthesis of information when searching for ideas for solving assigned problems.
In conditions of limited funding for solving any socio-economic problem, assessing the relationship between the desired and the actual becomes essential. Forecasting as a control element does not immediately bring any material results. At the same time, the costs for each stage must be strictly regulated. To obtain the best possible solution based on the cost criterion, the method of functional-cost analysis is used.
As an auxiliary creative element in forecasting, the method can be successfully used at all stages, especially in conditions of uncertainty and when deadlock situations arise when other methods do not allow obtaining a satisfactory result.
The simulation-based forecasting process includes several main steps
The second stage is the identification of significant factors influencing the forecast object
The second group of problems that occupy a central place in the work is related to the specifics of the specific energy resource policy pursued by Arab progressive states that are at the stage of a national democratic revolution - world exporters of liquid fuels. As a central link in their national strategy of economic and socio-cultural Construction, as well as as a foreign policy instrument, oil policy is formed taking into account a variety of numerous parameters, macroeconomic and sectoral, while simultaneously being influenced by a number of non-economic factors. Possibly more complete coverage of this complex set of cause-and-effect relationships is the most important guarantee of the depth of its analysis and the reliability of forecasting. These elements undoubtedly need systematization. But excessive schematicism and enthusiasm for modeling are sometimes more dangerous than excessive caution in generalizations. Often similar, and sometimes identical, objective prerequisites serve as the basis for fundamentally different raw material strategies, which was very clearly visible in Iraq in the 60s and Libya at the turn of the 70s. This once again convinced the author of the need for special attention to the social and class origins of oil policy, which, other things being equal, often determine its character, as well as the character of subjective factors in general. The same point significantly influenced the choice of countries - the main subjects of study in this work.
Poor integration with other systems. The stages of goal setting and results verification should be combined with other measures, such as forecasting, budgeting and other processes.
A huge number of forecasts developed in various sciences in economics, social sphere, ecology, necessitates their typology, classification and systematization according to characteristic features. There are various classifications of geographic forecasts depending on approaches, time depth (lead time), territorial coverage and other characteristics. There are search, normative and integral forecasting. the main objective search engine(genetic, resource) forecasting consists in finding out the ways of development of an object or process while maintaining existing trends. It is assumed that the observed trends cannot be changed by a volitional decision. Normative forecasting based on determining the optimal development option for the facility in the future within the framework of scientifically based needs and standards. Its task is to determine the ways and timing of achieving the desired state of the object in the future, in accordance with the goal. Integral forecasting arose at the intersection of these two types of forecasting and is used to develop targeted comprehensive programs for the development of regions and cities.
In terms of scale, geographical forecasts can be global, regional and local.
Based on their content, they distinguish between partial and integral geographical forecasts. Particular forecasts are necessary to solve such problems as justifying the involvement of natural resources in economic circulation, forecasting the development of intersectoral complexes and territorial socio-economic systems of various hierarchical ranks, improving the population settlement system, internal and external economic relations, developing plans for the social development of cities and regions, justification for recreational activities, etc. The totality of all particular geographical forecasts is an integral forecast.
The development of geographic forecasts is a sequence of several logically interconnected stages: 1. Setting the goals and objectives of the study. 2. Determination of the chronological and territorial scope of the study. 3. Collection and systematization of all information about the functioning and development of territorial systems and their functional subsystems. 4. Construction of a “tree of goals”, selection of forecasting methods, identification of limitations and inertial aspects of the development of the predicted object or process. 5. Development of private geographical forecasts: natural resources, territorial organization of productive forces, intersectoral complexes, population and settlement system, etc.
The system of main stages of geographical forecast includes theoretical and information support of the forecast, analytical work and choice of method, as well as ensuring the reliability of the forecast (forecast verification).
The theoretical support for the forecast is based on the latest achievements in geography. It is based on the doctrine of geosystems formed under the influence of natural and anthropogenic factors. These factors determine the dynamism, stability and nature of relationships in territorial systems. When they are violated, irreversible changes occur in geosystems, the study of which is of great importance for forecasting.
Information support for the forecast is based on collecting information on theoretical issues of forecasting in relation to a specific object and obtaining specific information about it. Information materials can be obtained both as a result of special studies (expeditionary, stationary, semi-stationary), and in statistical bodies, in scientific reports, literature, etc.
The reliability and accuracy of the forecast depend on the level of development of theoretical knowledge about the predicted object, the degree of completeness of the information used, and the correctness of the formulation of the problem of choosing a research method. To verify the forecast, the following approaches are used:
1. Deeper knowledge of the structure, functions and interrelations of the forecast object, mechanisms of formation and development of natural and socio-economic processes and phenomena.
2. Testing forecasting methods and techniques on similar objects.
3. The use of several methods and techniques for making a forecast to establish the degree of agreement of the forecast results.
4. Dividing the actual series of observations of the predicted process into two parts in order to use one part to predict the other.
5. Using the method of expert assessments.
6. Synthesis of partial geographical forecasts.
7. Development of basic forecast options.
8. Constructing a preliminary forecast.
9. Examination and preparation of the final forecast.
10. Forecast adjustment.
11. Using forecasting results to solve theoretical and practical problems of geography.
An important task of geographical forecast is search for stable connections (structural, functional, spatial, temporal, etc.) between the components of geosystems. This is due to the multidimensionality of the forecasting object - the territorial system of a certain region. To overcome the barrier of multidimensionality, it is necessary to use the following approaches to general scientific forecasting: 1) decomposition techniques, i.e., breaking the whole into component parts that are more simple and accessible to research; 2) the use of simple indicators that reflect the most important forecast factors or their sum; 3) aggregation, i.e. combining several indicators into one. Therefore, in geographic forecast simultaneously applies synthesis and analysis of natural and socio-economic processes and phenomena.
Geoforecasting methods
The purpose and object of the forecast determine the choice of its methods. Under geographic forecasting methods methods of theoretical and practical development of forecasts are understood. There are a large number of methods for economic-geographical forecasting, and their number is constantly growing. The choice of one or another forecasting method depends on the purpose of the study, the information base, and the nature of processing the initial information.
Therefore, certain methods correspond to each specific study and stage of forecasting. These methods can be divided into three groups: general scientific(analysis and synthesis, induction and deduction, extrapolation and interpolation, analogy, experiment, etc.), interscientific(modelling, operations research, statistical, expert assessments, etc.) and private scientific(assessment of the prospects of the geographical location, functional zoning of the territory, cartographic, etc.). Let's look at the most common methods of geographic forecasting.
Logical methods. These methods are based on the use of a certain sequence of mental operations. Their wide distribution in the study of territorial systems is due to their great complexity, the diversity of relationships between natural and economic systems, and the long time it takes for forecast objects to form.
General scientific logical methods include the methods of induction and deduction. By induction cause-and-effect relationships between objects and phenomena are established. The study is carried out from the specific to the general by determining the similarities and differences in the development of the object. In forecasting, this method is used to obtain probabilistic judgments with an insufficient information base, that is, in the absence of a long series of statistical data.
Deduction method represents a transition in the process of cognition from the general to the particular and individual, the derivation of the particular and individual from the general. This method is used to determine the forecasting strategy.
Widely used in geographic forecasting intersystem analysis method, proposed by A.L. Chizhevsky back in the 20s for two periodically related systems - solar activity and the rhythms of natural processes. The 11-year period of solar activity is noted as the main period that influences many natural processes of the Earth - river flow and floods, avalanches and mudflows, landslides and dust storms and others. This period is used to predict many spontaneous natural processes. Deviations from 11-year cycles are explained both by the properties of the natural processes themselves and by the perception of solar rhythms by a specific natural and economic background, the underlying surface of the Earth. This makes it necessary to predict natural processes taking into account local landscapes and economic characteristics of the region.
Methods of expert assessments. These methods are used in conditions where there is no sufficient theoretical basis (justification) for the development of the object. Their use is also justified in cases where there is no representative and reliable statistics of the characteristics of the object, there is great uncertainty in the operating environment of the object, when forecasting socio-economic objects that are strongly influenced by scientific and technological progress, as well as when carrying out forecasting under time pressure.
Software forecasting method involves developing a classification of the type of events that need to be analyzed and an initial list of experts on the problem under study. For each type of problem, the authority of each expert is determined on a 100-point scale using objective methods. At the first stage, the problem is stated by listing events, the time and probability of which are called final. The scenario of these events is given to experts who have the highest “weight” on this issue. Experts determine the conditions under which assessment of these events is possible. Then the probability of the event occurring and the probable amount of time between the time the condition is met and the time the event occurs are estimated. The final forecast of the occurrence of this event is made on the basis of averaging the assessments of individual experts, taking into account their “weight”.
Heuristic forecasting method named in connection with the homogeneity of the forms of mental activity of the expert. This method is used to obtain ideas about the prospects for the development of a narrow field of science and technology based on systematic processing of forecast estimates from groups of experts.
The method of collective generation of ideas, or the “brainstorming” method. When using this method, an avalanche of new ideas is put forward and the creative potential of a group of specialists is activated. This is achieved as follows:
Each participant gets the opportunity to see the problem posed through the eyes of his colleagues;
Collective creative thinking skills are developed.
Summing up is carried out collectively. The following tasks are solved:
Receive final answers to the questions posed;
A plan for solving relevant problems is formed;
Ideas that can be used to solve a particular problem are selected;
New aspects of the problem under study are established.
Another method of expert assessments is the PATTERN method. At the initial stage, the development trends of the predicted object are studied and their expert assessment is given to obtain judgments about possible ways of changing the object. Then the optimal options and means of achieving the main objectives are determined. To do this, a scenario for the development of the predicted object is drawn up. Scenario - it is a way of determining the logical sequence of probabilistic events to establish development alternatives. Event - it is an action that may or may not occur if a certain set of conditions are met. This method is widely used in solving problems of forecasting scientific and technological progress and the development of industrial sectors.
Goal tree method. A goal tree is a systematic record of the stages of solving a problem. The final goal is divided into intermediate stages, each of which is necessary to solve the previous task. Each node of the goal tree is divided into several branches with elements ranked by degree of importance in terms of achieving the immediate goal.
One of the oldest methods of knowledge is widespread in geographic forecasting - method of analogies. A forecast by analogy is a conclusion made about the properties of the predicted object based on its similarity with other objects both in structural and genetic characteristics, i.e., a given spatio-temporal situation is compared with some past historical situation. Using this method, the predicted parameters, timing and significance of expected events are clarified. The main stages of the analogy method are the search and selection of an analogue, construction of a model and its study, extrapolation of data from the analogue to the object under study, verification of extrapolation conclusions by analogy.
Popular in forecasting genetic method based on the analysis of spatio-temporal evolutionary stages of development of phenomena and processes that explain observed facts and suggest still unknown ones. In physiographic forecasting, this method is interpreted as method of landscape-genetic series. Knowing the sequence of spatial changes in natural complexes within the genetic series, it is possible to predict the order of their changes in the process of development. Using these and other forecasting methods, it is possible to outline trends in future changes in the natural environment under the influence of natural and anthropogenic factors with a probability of about 60-65%.
Statistical forecasting methods are aimed at identifying time-stable characteristics of the predicted object, searching for patterns of its development and studying the state to determine the main directions of change of the object in time and space.
The greatest development of the formalized forecasting methods was the method extrapolation of development trends. The extrapolation method is a classic popular forecasting method, based on finding the probabilistic value of the predicted object at a given time using known characteristics. To do this, determine the development trends of the forecast object, i.e., the development trends of the natural environment in the past and future, taking into account not only its stable development or the preservation of absolute increases in the predicted values, but also their possible acceleration or even the emergence of new factors limiting or stimulating development.
Solving the extrapolation problem involves finding, from known qualitative and quantitative values, the probabilistic value of the predicted indicator at a certain point in time, taking into account the duration of the forecast period. The predicted process consists of regular and random components . The first value represents the trend component. The second is considered an uncorrelated random process and is necessary to adjust the forecast characteristics. The main focus is on the process of best describing the trend, on the basis of which forecast extrapolations are built. The choice of the trend that most adequately describes the predicted process is associated with the determination of the appropriate type of functions. To construct predictive functions, information is needed about stable relationships, the pace and direction of processes over a long period of time, the properties of processes at a certain moment, and the initial and limiting conditions of the development process.
It is also important to correctly determine the extrapolation lag (extrapolation range). The depth of forecast extrapolation should not exceed half of the period taken as the base, i.e., for example, for a 10-year forecast, a time series of 25-30 years is required. The reliability of the resulting forecast is determined by the probability of the predicted event occurring.
Other formalized methods of geographic forecasting are correlation, regression, factor analysis, the method of envelope curves, etc.
Correlation analysis- this is the definition of the relationship between two quantities, expressed in the fact that when one quantity changes in a certain direction, the other also changes. Regression analysis consists in identifying the functional dependence of the average value of one value on one or more variables. Factor analysis allows you to “compress” a large number of initial indicators into a smaller number of generalized characteristics (factors) with the loss of a small amount of initial information. Envelope curve method is based on identifying trends in changes in the parameters of the predicted object under different conditions that determine the limits of growth. The main development trends are plotted on a graph, and then an envelope curve is drawn along the inflection points of the curve, which represents a general tendency for the object to change over time. This method is especially effective for obtaining short-term forecasts of changes in the technical and economic indicators of technological processes and changes in the level of environmental pollution from sources of different power.
To develop economic and geographical forecasts, modeling, in particular mathematical modeling, is increasingly being used. It is necessary to create adequate predictive models of the objects, phenomena and processes being studied. Modeling allows us to identify the causality of system parameters and give a functional, point and interval assessment of them.
Among the existing models for forecasting purposes, the following models are used:
1. Functional, describing the functions that are performed by individual components of the system and the system as a whole.
2. Physical process models, defining mathematical relationships between the variables of this process. They can be continuous and discrete in time, deterministic and stochastic.
3. Economic, determining the relationship between various parameters of the process and phenomenon being studied, as well as criteria that allow optimizing economic processes.
4. Procedural, describing the operational characteristics of systems necessary for making control decisions.
Predictive models can be conceptual(expressed by verbal description or flowcharts), graphic(presented in the form of curves, drawings, maps), matrix (as a link between verbal and formalized representation), mathematical(presented in the form of formulas and mathematical operations), computer(expressed in a description suitable for entering into a computer).
A special place is occupied simulation predictive models. Simulation modeling is the formalization of empirical knowledge about the object under consideration using modern computers. Under simulation model is understood as a model that reproduces the process of functioning of systems in space at a fixed point in time by displaying elementary phenomena and processes while preserving their logical structure and sequence. This allows, using initial data on the structure and main properties of territorial systems, to obtain information about the relationships between their main components and to identify the mechanism for the formation of their sustainable development.
The process of developing geoecological forecasts based on mathematical modeling includes the following stages:
1. Formulation of the purpose and objectives of the study. Qualitative analysis of the predicted object in accordance with the purpose of the study.
2. Determination of the subject and level of modeling, depending on the forecasting tasks.
3. Selection of the main features and parameters of the model. The model should include only parameters that are essential for solving a specific goal, since an increase in the number of variables increases the uncertainty of the results and complicates the calculations of the model.
4. Formalization of the main parameters of the model, i.e. mathematical formulation of the goals and objectives of the study.
5. Formalized representation of the relationships between the parameters and characteristics of the predicted object or process.
6. Checking the adequacy of the model, i.e., the accuracy of the mathematical model’s reflection of the features of the original.
7. Determining the informative capabilities of the model by establishing quantitative connections between patterns.
Lecture No. 10
Field concept in geography
Main issues discussed in the lecture:
1. The concept of field in geography.
2. Maps of fields and their varieties.
3. General rules for creating field maps.
4. Maps of fields of continuous and discrete phenomena.
5. Cartographic-statistical method and field maps.
6. Field maps and modeling method.
7. Mathematical-statistical and isolinear models as a tool for analysis and synthesis of the studied indicators.
1. Concept of field in geography there is a system of ideas about real and abstract fields and surfaces, about methods of their cartographic representation. It is intended for the creation and use of cartographic models of fields for scientific and practical purposes (Chervyakov, 1992).
Currently, the field concept has seriously interested representatives of various sciences - geophysicists, meteorologists, hydrologists, geographers, demographers, sociologists, geologists, linguists, etc. This can be explained, on the one hand, by the noticeable benefits of using physical analogies, and on the other hand, by the possibility of widely using mathematical apparatus and map as a means of obtaining, storing, transforming and visualizing various quantitative information about natural and socio-economic phenomena.
Physicists usually consider a field to be a space in which forces of one kind or another act. Hence physical fields are often called force fields. It is no coincidence that the geophysical field of the Earth that is closest to geographers is considered to be the space in which the forces associated with the earth’s matter, its movement and the processes occurring in it act.
Another, abstract mathematical concept of a field presupposes the existence of space, at each point of which the numerical value of a certain quantity is determined. In this case, the field is considered as a function of the position of the point in space and time. In this form, the scope of the concept “field” expands significantly. It covers not only natural, but also socio-economic phenomena. The first includes the spatial distribution of atmospheric pressure, temperature, precipitation, the second - the location of the population, natural resources, production, and institutions serving the population.
Finally, a field is often understood as the area of distribution of any phenomena expressed not only quantitatively, but also qualitatively, not only in analytical, but also in synthetic indicators. Defining such a field is not an easy task. In content, but, perhaps, it comes closer to such universal philosophical categories as “space”, “object”, “phenomenon”.
Based on the above, we will assume that there are three main ideas about the field: 1) physical (field as an area of distribution of forces, energies, interactions); 2) abstract mathematical (the area of distribution of values that characterize the region from various angles); 3) abstract-logical (the area of distribution of any phenomena and their indicators in both qualitative and quantitative terms).
Geographers who adhere to the physical (force) concept of the field note the importance of using the physical concept in geographical research (gravitational field), which arises around some source of “power” (for example, an industrial enterprise or a populated area). These conditionally force fields are often considered as a result of the interaction of many homogeneous objects (“tel” - settlements, factories, mines) that differ from each other "mass" - quantitative characteristics (population, volumes of natural resources, manufactured products, etc.). In population geography, such “bodies” are often taken to be the population of points, and the “mass” is the population size. “Gravitational fields” or fields of potentials of this kind are used in economic geography to study not only the population, but also production, transport links, service elements, fixed assets and other phenomena. Geographic fields are considered as a source of connections in geosystems, they try to find analogues of electrostatic and gravitational fields in their structure and functioning, they propose to identify the conditions for the emergence of flows of matter, energy and information, and find their sources.
The abstract-mathematical (quantitative) representation of the field penetrated into geography and became widespread in it thanks to the close connections of geography with other sciences about the Earth and, above all, with geophysics, which studies, with the help of fields, the processes occurring in the solid, liquid and gaseous shells of the Earth. “Field” is an integral part of the vocabulary of a meteorologist and hydrologist, which they use when studying the spatial distribution of air and soil temperatures, atmospheric pressure, precipitation and other meteorological elements. The undoubted merit of geophysicists and hydrometeorologists can be considered the fact that, on the one hand, they accepted the abstract mathematical concept of a field, extended it to a wider range of natural phenomena and developed a fundamental methodological basis for the mathematical analysis of fields; and on the other hand, they created conditions for the effective use of field theory in other geosciences, including a cycle of branch geographical disciplines covering both nature and society.
The abstract-logical (non-quantitative) concept of a field is quite popular among geographers, which is explained by the exceptional complexity of geographical objects, which makes it difficult to parameterize phenomena. There is also an underestimation of the importance of actively introducing quantitative and other mathematical approaches into geography.
Without denying the possibility of considering the concept of field in geography from three noted sides (physical, abstract-mathematical and abstract-logical), when solving problems of interaction between nature and society, preference should be given to the second side. Indeed, the physical interpretation is characterized by narrowness and inability to cover the entire diversity of natural and especially socio-economic phenomena. The abstract-logical interpretation is too broad, vague and not always amenable to mathematical description. Experience shows that fundamental concepts are successfully introduced into science and practice after the problem of measuring and calculating the signs being studied has been solved. It is no coincidence that the abstract mathematical (quantitative) description of fields predominates in the exact sciences.
Continuity of distribution of the studied quantitative characteristics is an attribute of any field. Hence, it is legitimate to call the area of continuous distribution of quantitative characteristics a field. “Topographic” and “industrial” reliefs, “statistical” and trend (smoothed) surfaces are the essence of the geometric image of their fields, which outwardly resemble the relief of the earth’s surface. Of all the possible methods of cartographic representation of fields, and therefore surfaces, the main one is the isoline method, which has increased clarity, special metricity, information content (the ability to take information at any point, relief imagery (the ability to perceive various indicators of continuous and discrete phenomena in the form of the relief of the earth's surface) , low sign load of cards Hence. field map it is legitimate to call a special group of maps intended for isolinear display of a continuous, smooth, smooth territorial distribution of quantitative characteristics characterizing both natural and socio-economic phenomena.
2. Maps of fields and their varieties. It is known that physicists divide fields into two large groups scalar And vector. A scalar field is a region of space, each point of which is described by its own value of a quantitative attribute. To describe points in space vector fields Two vector characteristics are required - a numerical value (module) and the direction of movement. The concept of this field arose in physics mainly in the study of the velocities of movement of liquid particles, the strength of lines of force (magnetic and electric), shifts of points of an elastic body, etc.
According to these two groups of fields, we select maps of scalar and maps of vector fields. Maps of scalar fields are directly related to the concept of “statistical surface” and to isolines as the most effective means of cartographic representation of these fields. Methods for displaying vector fields on maps are less developed. However, perhaps the most suitable here are arrows that can combine two characteristics - module and direction.
Based on the method of obtaining quantitative information, field maps can be divided into field maps of field observations and field calculation maps.
Field maps of field observations are compiled based on direct instrumental measurements of field parameters (scalar and vector). These include measurements of the relief of the earth's surface, geological and soil structure, meteorological and hydrological indicators.
Calculation field maps are compiled as a result of preliminary mathematical (usually mathematical-statistical) processing in office conditions of various quantitative information collected in the field or taken from maps and images, obtained from statistical reporting materials.
Both time and territorial series can be subjected to mathematical and statistical processing. In the first case, continuous distributions of such indicators as average monthly air temperature, standard deviation of precipitation by year, annual increase in grain yield are calculated and mapped, and in the second case - data localized at points, on lines and areas, which are statistically generalized throughout the entire study area. territory or in individual territorial cells. In this case, not average monthly or average annual indicators are obtained, but indicators averaged over territorial cells, for example, average temperatures, precipitation by region.
Considering the orientation of modern sciences towards the study of objects as systems consisting of individual dynamic and interconnected elements, it is advisable to divide the entire variety of field maps of natural and socio-economic phenomena into maps of fields of statics, dynamics and relationships between phenomena. If the second group of field maps shows in what direction and with what intensity the development of phenomena occurs, then the third group - interconnection field maps - answers the question of what factors and to what extent determine the existing spatial structure of the objects and phenomena being studied.
3. General rules for creating field maps. Despite the wide variety of field maps, when compiling them, one should be guided by the following general rules, which are based on the property of a continuous continuous distribution of scalar and vector characteristics of the mapped fields, as well as the fundamental impossibility of making measurements at all points of the terrain.
Rule one - mandatory preliminary measurement (for maps of calculated fields) of scalar and vector characteristics at selected points of the terrain.
Rule two - potential ability to determine the characteristics of fields at any point in the area (map).
Rule three - representative (representative) selectivity of measurements and calculations at points. Indeed, it is not possible to determine cartographically and reproduce scalar and vector characteristics at an infinite number of terrain points. We have to limit ourselves to selective measurements on regular or irregular grids of points, which are often called control points. When these points are intended for drawing isolines, it is more correct to call them reference points.
Rule four - reproduction in point measurements/calculations of continuous properties of fields, which is manifested in determining the gradualness of changes in quantitative characteristics between adjacent control (reference) points, in the absence of sharp jumps and infinitely large values.
Rule five - dissemination of data obtained at some points to the entire mapped territory. This is most often done using conventional cartographic interpolation.
4. Maps of fields of continuous and discrete phenomena. With the help of isolines, the relief of the earth's surface, territorial distributions of atmospheric pressure, temperature, precipitation, magnetic declination and other truly continuous phenomena have been successfully mapped for a long time. However, these maps of continuous phenomena, constructed, as a rule, based on field measurements, reflect only part of the natural indicators usually obtained on the ground. Isolinear display of such discrete, continuous, geographically isolated phenomena , such as natural resources, population, agricultural and industrial production, is not sufficiently accurate and reliable. This can be explained by the fact that the isolines here were constructed not according to traditional point observations, but according to area indicators, only conditionally attributed to the centers of the corresponding territorial cells. It turned out that the quantitative indicators at the center points did not meet the rule of unambiguity of numerical values. The latter largely depend on the size, shape and orientation of the territorial cells of the source data localization. Hence, cartographers were faced with the task of developing a more advanced methodological apparatus for creating isolinear maps from discrete data, which makes it possible to determine mapped quantities at any point in the area. It is only such maps that can be rightfully called maps of fields of discrete phenomena.
Solving this problem made it possible to significantly expand the range of isolinear field maps and create more favorable conditions for the comprehensive study of complex geographical objects, pairing isolinear maps of natural and socio-economic, continuous and discrete phenomena. Hence, cartographers faced the second task of developing a system of methodological techniques for compiling maps of fields of different content, different spatial and temporal affiliations. The ability to take data at any point and in any volume has created favorable conditions for comparing the maps in question not only visually, but also at the level of mathematical processing of cartographic information.
Each of the two considered problems has its own theoretical foundations that stimulate the development of new types of maps and mapping techniques. Thus, on the basis of the dialectical unity of discreteness and discontinuity, the legitimacy and expediency of extending the field concept to many natural and socio-economic phenomena, the absolute spatio-temporal discreteness of which was previously not in doubt, was proven (Chervyakov, 1978). For this purpose, a new type of maps of fields of discrete phenomena was proposed, the core of which was maps of density fields, but
Scientifically based forecasting is an important tool of modern management. It is used both for strategic planning of the development of individual enterprises and for the development of long-term socio-economic programs at the state level. The structure and stages of this process are closely related to the methodology and the adopted model.
Forecasting is a system of theoretically based ideas about the possible future states of an object and the directions of its development. This concept is similar to the term hypothesis, but, unlike the latter, it is based on quantitative indicators and has greater reliability. The common feature of these two concepts is that they explore an object or process that does not yet exist.
Applied forecasting techniques received active development in the 70s. XX century, and the boom in their use abroad continues to this day. This is mainly due to a new direction in research - global issues, the main task of which is to solve the world's resource, demographic and environmental problems.
Forecasting is a science that has a close relationship with statistics and its analytical methods. When carrying out the analysis, the achievements of mathematics, natural and other sciences are widely used.
Forecasting and planning complement each other in various variations. In most cases, the forecast is developed before the plan is created. He can also follow the plan to determine possible consequences. In large-scale studies (at the state or regional level), the forecast can act as the plan itself.
The main task of forecasting is to identify effective ways to manage socio-economic processes in society or the economic and technical development of an enterprise.
The methodological basis for achieving such goals are the following:
Forecasting is carried out according to a certain methodology, which is understood as a system of indicators and approaches to the object under study, and the logic of research. Other parameters also depend on which methodology is chosen - how many stages of forecasting will be carried out and what their content will be.
Among the huge number of forecasting methods, the following main groups can be distinguished:
1. Individual expert assessments:
2. Collective based on obtaining a consensus opinion in a group of experts:
3. Formalized methods based on the use of mathematical methods of assessment:
4. Complex techniques that combine several of the above:
The correctly chosen forecast method significantly affects its errors. For example, strategic planning does not use extrapolation (foreseeing beyond experimental data or extending properties from one subject area to another).
The sequence of forecasting stages in the general case represents work according to the following scheme:
Below is a description of the main stages of forecasting and their characteristics.
At the first stage, the following questions are resolved:
At the stage of forecasting, the performers who must carry out the forecast are also determined. This group may consist of competent employees responsible for organizational work and information support, and also includes an expert commission.
The following points are documented:
At the second, analytical stage of forecasting, the following types of work are carried out:
In the process of analysis, the current state of the object is diagnosed and trends in its further development are determined, and the main problems and contradictions are identified.
The stage of identifying other, most likely options for the development of an object is one of the key stages of forecasting. The accuracy of the forecast and, accordingly, the effectiveness of decisions made on its basis depend on the correctness of their determination.
At this stage the following work is performed:
Based on available information and previously conducted analysis, an expert study of an object, process or situation is carried out. The result of this forecasting stage is a reasonable conclusion and identification of scenarios under which development will be most likely.
The examination can be carried out using various methods:
A forecasting model is a simplified description of the object or process under study, which allows one to obtain the necessary information about its future state, directions for achieving such a state, and the interrelations of individual elements of the system. It is selected based on the research method.
In economics, there are several types of such models:
There are also other classifications of models:
In forecast models, the following forms of description of phenomena are distinguished:
The model is formed using the following methods:
After selecting the model, a forecast is made for certain periods. The results obtained are compared with currently known information.
The forecast verification stage, or checking its reliability, is carried out on the basis of previous experience (a posteriori) or independently of it (a priori). Quality assessment is done using the following criteria: accuracy (dispersion of forecast trajectories), reliability (probability of the chosen option being implemented), reliability (measure of process uncertainty). To assess the deviation of forecast criteria from their actual values, a concept such as forecast errors is used.
The controlling process also involves comparing results with other models and developing recommendations for managing an object or process, if such an impact may have an impact on the development of events.
There are 2 methods for quality assessment:
The accuracy of the forecast is influenced by the following main factors:
Often large errors also arise due to the fact that the specific conditions in which the model is applied are not taken into account.
The last stage of forecasting is the implementation of the forecast and monitoring the progress of its implementation. If critical deviations are identified that can significantly affect the further development of events, the forecast is adjusted.
The level of adoption of amendment decisions may vary. If they are insignificant, then the adjustment is carried out by the analytical group, which is responsible for developing the forecast. In some cases, experts are involved in this work.
Prognostics is a scientific discipline that studies the general principles and methods of forecasting the development of objects of any nature, the laws of the process of developing forecasts. How the science of prognostics was formed in the 70s - 80s of the twentieth century. In addition to the concept of “prognostics”, the term futurology is used in the literature. Like any science, prognostics has a set of its own terms used to denote certain concepts. Definitions of prognostic concepts were recorded in 1978.
Forecast (from the Greek rsgnshuit - foresight, prediction) - prediction of the future using scientific methods or the result of the prediction itself, an informed judgment about the possible state of an object in the future or alternative ways and timing of achieving these states.
Forecasting is a proactive reflection of the future; a type of cognitive activity aimed at determining trends in the dynamics of a specific object or event based on an analysis of its state in the past and present.
Forecasting, development of a forecast, in a narrow sense, is a special scientific study of specific prospects for the development of a process.
The purpose of creating a forecast is to reduce the level of uncertainty within which the manager must make decisions. This goal dictates two basic rules that the forecasting process must follow.
1. Forecasting must be technically correct and must generate forecasts that are sufficiently accurate to meet the needs of the firm (enterprise).
2. The forecasting procedure and its results must be fairly effectively presented to the manager, which will ensure the use of forecasts in the decision-making process for the benefit of the company (enterprise). Forecasting results must also be balanced in terms of cost/benefit.
Forecasting procedures are classified as quantitative and qualitative. At one pole here is a purely qualitative apparatus that does not require explicit mathematical manipulation of data. Only the "estimate" provided by the forecaster is used. Of course, even in this case, the forecaster's "estimate" is really the result of a mental analysis of the data. At the other pole is a purely quantitative apparatus that does not require any additional assessment. These are purely mechanical procedures that produce quantitative results.
Forecasting uses three main complementary sources of information about the future: assessment of the prospects for the development of the phenomenon under study based on experience, most often based on analogy with already studied similar phenomena and processes; conditional continuation into the future of trends, the patterns of development of which in the past and present are quite well known (extrapolation); creating a model of the future state of the phenomenon or process under study in accordance with the expected or desired change in a number of conditions, the development prospects of which are quite well known.
The following forecasting methods correspond to these sources: 1) Survey of the population and experts (questionnaires, interviews) for the purpose of objectification and streamlining of individual forecasting assessments. 2) Extrapolation and interpolation, i.e. construction of dynamic series of indicators of the predicted phenomenon during the periods of base of the forecast in the past and anticipation of the forecast in the future (retrospection and prospection of forecast developments). 3) Modeling - construction of search and normative models taking into account the probable or desired change in the forecasted phenomenon for the forecast period based on available direct or indirect data on the scale and direction of changes.
The forecasting stage is part of the forecast development process, characterized by its tasks, methods and results. The division into stages is associated with the specifics of constructing a systematic description of the forecast object, collecting data, building a model, and verifying the forecast.
A typical forecasting technique consists of the following main stages of research:
1) Pre-forecast orientation - determination of the object, subject, problem, goals, objectives, lead time, working hypotheses, methods, structure and organization of research). The first stage involves identifying the final application forecasting purposes; a set of factors and indicators (variables), a description of the relationships between which must be made; the roles of these factors and indicators - which of them, within the framework of the specific task set, can be considered input (i.e., fully or partially regulated or at least easily recordable and predictable; such factors carry the semantic load of explanations in the model), and which - weekends (these factors are usually difficult to predict directly; their values are formed as if in the process of functioning of the modeled system, and the factors themselves carry the semantic load of the ones being explained).
Stage 2 (a priori, pre-model) consists of preceding the construction of the model analysis meaningful essence of the process or phenomenon being studied, formation and formalization of available a priori information about this phenomenon in the form of a number of hypotheses and initial assumptions (the latter should be supported by theoretical reasoning about the mechanism of the phenomenon being studied or, if possible, by experimental testing).
The 3rd stage (information and statistical) consists of collection necessary statistical information, i.e. recording the values of the factors and indicators involved in the analysis at various time and (or) spatial cycles of the functioning of the modeled system. Data collection involves obtaining correct data and mandatory verification that it is correct. This stage is often the most questionable part of the entire forecasting process and at the same time the most difficult to test, since subsequent stages can equally well be carried out using data either relevant to the problem under study or not. Whenever there is a need to obtain certain data from an organization, its collection and verification are necessarily accompanied by many different problems.
Stage 4 (model specification) includes direct conclusion(based on the hypotheses and initial assumptions adopted at the 2nd stage) of a general form model relations, connecting the input and output variables of interest to us. Speaking about the general form of model relationships, we mean the fact that at this stage only the structure of the model will be determined, its symbolic analytical notation, in which, along with known numerical values (represented mainly by initial statistical data), there will be quantities whose meaningful meaning is defined, but the numerical values are not (they are usually called model parameters, the unknown values of which are subject to statistical estimation).
Stage 5 (identifiability study and model identification) consists of conducting statistical analysis of the model in order to “adjust” the values of its unknown parameters to those initial statistical data that are already available. A forecasting model is a model of a forecasting object, the study of which allows one to obtain information about the possible states of the forecasting object in the future and (or) the ways and timing of their implementation.
When implementing this stage, it is necessary to first answer the question of whether it is, in principle, possible to unambiguously restore the values of the unknown parameters of the model from the available initial statistical data given the structure (method of specification) of the model adopted at the 4th stage. This constitutes the so-called model identifiability problem. And then, after a positive answer to this question, it is necessary to solve the problem of identifying the model, i.e. propose and implement a mathematically correct procedure for estimating unknown values of model parameters using the available initial statistical data. If the problem of identifiability is solved negatively, then they return to stage 4 and make the necessary adjustments to the solution of the model specification problem.
The 6th stage (model verification) consists of using various procedures for comparing model conclusions, assessments, consequences and conclusions with reality. This stage is also called the stage statistical analysis of the accuracy and adequacy of the model. If the results of this stage are unsatisfactory, it is necessary to return to stage 4, and sometimes to stage 1.
In the description of the content of the 1st stage of the forecasting procedure, we discussed, in particular, the need to determine the final application goals of forecasting. This implies, in particular, the determination of the required type of forecast. The type of forecast is determined by two factors: the forecast horizon and the hierarchical level of the forecast indicator.
The forecast horizon is the maximum possible lead time for a forecast of a given accuracy. According to the forecasting horizon, forecasts are divided into short-term (1-2 time steps ahead), medium-term (3-5 time steps ahead) and long-term (more than 5 time steps ahead). Long-term forecasts are necessary in order to outline the main course of the enterprise for a long period, therefore it is on them that the main attention of senior managers is focused. Short-term forecasts are used to develop immediate strategies. They are most often used by middle and lower level managers to meet the needs of the near future.
Based on the level of the predicted indicator, it is advisable to distinguish macro-, meso- and microforecasts. Everything related to forecasting indicators characterizing the activities of firms, companies and enterprises belongs to the micro level. Meso- (regional and sectoral levels) and macro forecasts are used to describe the external environment.
Since it is with the help of expert forecasting that most of the problems that arise when developing forecasts can be solved, let us consider in a systematic way the main stages of expert forecasting:
1. Preparing to develop a forecast
2. Analysis of retrospective information, internal and external conditions
3. Determination of the most likely options for the development of internal and external conditions
4. Conducting an examination
5. Development of alternative options
7. Monitoring the progress of implementation and adjusting the forecast
Stage 1. At the stage of preparation for developing a forecast, the following tasks must be solved:
* organizational support for the development of the forecast has been prepared,
* a forecast task was formulated,
* a working and analytical support group has been formulated,
* an expert commission has been formulated,
* methodological support for forecast development has been prepared,
* an information base has been prepared for the forecast,
* computer support for forecast development has been prepared.
After making a decision to develop a forecast, it is necessary to assign executors for this development. This group of workers is entrusted with organizational support for the development of the forecast. They must also provide methodological and information support.
A high-quality expert forecast can be developed only when it is well prepared, if competent specialists are involved in its development, when reliable information is used, when estimates are obtained correctly and correctly processed.
To develop a high-quality forecast, it is necessary to use modern technologies that accompany and support the development process.
Specialists who are professionally familiar with the object of examination are invited to join the expert commission. If a multidimensional assessment of an object is required, or heterogeneous objects must be assessed and this requires specialists from different professions, then the expert commission should be formed in such a way that it includes specialists who are able to professionally assess all the main aspects of the predicted problem.
The task of the analytical group is to methodically prepare the forecasting process. The analytical group includes specialists with professional knowledge and experience in carrying out forecast developments. The development of the forecast must be carried out methodically correctly, the methods used must correspond to the nature of the forecasted situation and the nature of the information to be obtained, analyzed and processed. Also, the development of the forecast must be clearly regulated, that is, the working group must prepare the necessary documentation, which includes: an officially formalized decision to carry out the forecast, the composition of the expert commission, a forecast development schedule, contracts with specialists involved in its development, etc. Specialists must be provided with all the necessary information about the forecast object. An analytical review on the predicted problem, specially prepared by the analytical group, may be useful. When working with multivariate forecasts, you have to deal with large volumes of information, which, moreover, must be analyzed and processed in accordance with the forecast development technology used. This cannot be done without a computer and appropriate software.
Stage 2. When analyzing retrospective information about a forecasting object, a clear separation of quantitative and qualitative information is assumed. Quantitative information (sufficiently reliable) is used for calculations to extrapolate the dynamics of changes in the predicted parameters, to determine the most likely trends in their change. Qualitative information is classified, systematized and serves as the basis for expert assessments and is used to develop expert forecasts. When developing a forecast, it is necessary to analyze the internal conditions of the forecast object, a meaningful analysis of their features and development dynamics.
If mathematical, simulation, analog and other models of the functioning of the forecast object and changes in internal conditions have been developed, then the necessary data is entered into them and, on their basis, calculations are made to assess the most likely changes in the internal conditions of the forecast object.
When developing a forecast, no less attention should be paid to external conditions and the external environment of the functioning of the forecast object than to internal ones.
The internal environment, as an internal condition of the forecasting object, includes: intra-organizational processes, technology, personnel, organizational culture, management of functional processes. The external environment includes the general external environment and the immediate business environment of the organization.
Stage 3. Determining the most likely options for the development of internal and external conditions of the forecast object is one of the central tasks of developing a forecast. At this stage of forecast development, based on an analysis of internal and external conditions and all available information about the forecast object, information as a result of the work of the expert commission, a list of possible alternative options for changing internal and external conditions is preliminarily determined. After their preliminary assessment, alternative options are excluded from the list, the feasibility of which in the forecast period is questionable or the probability of their implementation is below a pre-established threshold. The remaining alternative options are subject to a more in-depth assessment in order to identify alternative options for changing internal and external conditions that are most likely to occur. At this stage of forecast development, the most active work of experts is expected to identify and assess key events that are expected to occur in the forecasted period of time.
Stage 4. The previous stage of forecast development provides the information necessary for the analytical group to conduct an examination. Experts are provided with information about the most likely changes in internal and external conditions, based on the previously conducted analysis, questions are formulated that should be answered as a result of the examination, and the most likely scenarios for the development of events are outlined.
Depending on the nature of the forecast object, on the nature of the assessments and judgments that must be obtained in the process of conducting the examination, specific methods for organizing and conducting the examination are determined. Examinations can be single-round or multi-round, anonymous and providing for an open exchange of opinions, etc.
A variety of methods are used in the comparative assessment of objects, in predicting the quantitative and qualitative values of the parameters of the predicted object, ranging from various modifications of the Delphi method to various procedures of the brainstorming method. The nature of the expert information that is supposed to be used in developing the forecast imposes certain requirements on the choice of a specific method for organizing and conducting the examination. If the predicted object is quite complex and multifaceted, then it is advisable to use complex methods of organizing and conducting an examination when conducting an examination to develop a forecast; the analytical group can use questionnaires and interviews.
Stage 5. The information prepared at the previous stages, including that received from experts, is used in the immediate development of the forecast. As a rule, cases are unlikely when it is known in advance in what direction changes in internal and external conditions will occur, what strategy will be chosen by the organization in a particular development of events. After all, the development of an organization in the projected future depends on various factors, as well as their combination and interaction. From this we can conclude that in strategic planning and in other cases of using forecasts, it is necessary to consider various alternative scenarios for the development of events, both favorable and unfavorable.
At the previous stages, the most likely changes in the main internal and external conditions that determine the course of the predicted events were determined. For the most probable alternative options, their changes, the most probable alternative options for the development of predicted events must be developed.
Stage 6. A priori and a posteriori assessment of forecast quality. Assessing the quality of the forecast is one of the central problems in the process of developing management decisions. The degree of confidence in the developed forecast largely influences the decision and affects the effectiveness of management decisions made using the developed forecast.
However, assessing the quality of a forecast is a rather difficult task not only at the moment when the forecast has just been developed (a priori assessment), but also at the moment when the predicted event has already occurred (a posteriori assessment). It should also be noted here that a qualitative forecast can be used in different ways when making a decision.
If the management of the organization does not have a significant influence on the course of events, but only monitors it, then after the end of the forecast period it is only necessary to compare the values of the predicted indicators and parameters with those obtained in reality. This allows us to evaluate the quality of the developed forecast a posteriori.
After developing a forecast, criteria must be determined by which the accuracy of the forecast can be assessed. Typically, two methods are used to evaluate the forecast: differential and integral.
The integral method involves a generalized assessment of the quality of the forecast based on an assessment of the quality of the forecast using specific criteria. With the differential method, sets of estimates of individual components of the forecast quality are evaluated, which have a fairly clear objective meaning. These criteria can be: clarity and clarity of the forecast assignment, compliance of the forecast with the assignment, timeliness of the forecast development, professional level of the forecast development, reliability of the information used, etc.
An example of the use of the integral method is the criterion “integral quality of expert forecast”.
The quality of the expert forecast is determined by the following criteria:
* competence (or, more generally, quality) of the expert;
* quality of information provided to experts;
* quality of expert information coming from experts;
* level of forecast development technology.
If the forecasting period has already ended, then it is necessary to compare the predicted values of indicators and parameters with those obtained as a result of the actual course of the predicted events.
And here the question comes to the fore - by what criterion to evaluate the quality of the forecast a posteriori. As an example of criteria for assessing the accuracy of a forecast, the following formula can be given: K1=¦X-И¦K2=¦lnX/И¦, where X is the predicted value of the indicator assessment; U is the true value of the indicator estimate.
Stage 7. Variant development of a forecast involves developing a forecast under various alternative conditions and assumptions. And they can change. Events that seemed unlikely yesterday are happening today, and events that seemed most likely are not happening. Therefore, an integral part of modern forecasting technology is periodic monitoring of the implementation of the predicted course of events, depending on the changes taking place. Monitoring allows timely detection of significant deviations in the course of events. If they can have a fundamental impact on the further course of events in terms of making important strategic decisions, then the forecast should be adjusted.
Adjustments can be of varying levels of significance, complexity, labor intensity, etc. If they are not very significant, then this problem can be solved at the level of the analytical group accompanying the development of the forecast. If the adjustments are more significant, then the additional involvement of individual experts may be required, and in particularly important cases, if there are significant changes, additional work of the expert commission with a possible change in its composition. The latter is necessary, especially in cases where the involvement of specialists of a different professional orientation is required to adjust the forecast.
Stages of economic forecasting at the enterprise level
The sequence of actions when developing each specific forecast may vary, but in general the whole process occurs in three main stages:
Forecast retrospectives are the forecasting stage, at which the history of the development of the forecast object and the forecast background is examined in order to obtain their systematic description.
At the retrospective stage, the following tasks are solved:
This stage usually includes the following work:
Predictive diagnosis is the forecasting stage, at which a systematic description of the forecast object and the forecast background is examined in order to identify dependencies.
At the diagnosis stage, the following tasks are solved:
At this stage, the following stages of research are carried out:
Prospection is the forecasting stage, at which, based on the results of the diagnosis, forecasts of the forecast object and the forecast background are developed, and the reliability and accuracy of the forecast is assessed.
The prospection stage involves obtaining forecast results based on all previous stages:
Note 1
This sequence in the development of forecasts is typical for forecasting methods based on mathematical modeling of objects. In the case of using expert forecasting methods, the composition and content of the stages changes somewhat.
The development of the forecast ends, as a rule, with the development of recommendations for decision-making. After a certain time, the forecast is examined, and based on its results, the forecast and recommendations are finalized.
The process of forecasting macroeconomic indicators can be represented as the following sequential stages:
Elementary:
Analytical:
Organizational:
Forecast:
Final:
