Classification of methods and models of forecasting. International Journal of Applied and Basic Research

Prior to the advent of modern IT, there were not many opportunities to use effective economic mathematical models directly in the process economic activity. In addition, the use of existing forecasting models for analytical purposes did not put forward such high requirements for their information support.

Fundamentals of forecasting technologies

When building a predictive system from scratch, it is necessary to resolve a number of organizational and methodological issues. The first ones include:

• - training of users in methods of analysis and interpretation of forecast results;
• - determination of directions for the movement of forecast information within the enterprise, at the level of its divisions and individual employees, as well as the structure of communications with business partners and authorities;
• - determination of the timing and frequency of forecasting procedures;
• - development of principles for linking the forecast with forward planning and the procedure for selecting options for the results obtained when drawing up an enterprise development plan.

The methodological problems of building a forecasting subsystem are:

• - development internal structure and the mechanism of its functioning;
• - organization of information support;
• - development of software.

The first problem is the most difficult, since to solve it it is necessary to build a set of forecasting models, the scope of which is a system of interrelated indicators. The problem of systematization and evaluation of forecasting methods appears here as one of the central ones, since in order to select a specific method, it is necessary to conduct them. comparative analysis. A variant of the classification of forecasting methods, taking into account the peculiarities of the knowledge system that underlies each group, can be summarized as follows: methods of expert assessments; methods of logical modeling; mathematical methods.

Each group is suitable for solving a certain range of tasks. Therefore, practice puts forward the following requirements for the methods used: they must be focused on a specific forecasting object, must be based on a quantitative measure of adequacy, and be differentiated in terms of the accuracy of estimates and the forecasting horizon.

The main tasks that arise in the process of creating a predictive system are divided into:

• - building a system of predictable processes and indicators;
• - development of an apparatus for economic and mathematical analysis of predicted processes and indicators;
• - concretization of the method of expert assessments, selection of indicators for examination and obtaining expert assessments of some predicted processes and indicators;
• - forecasting indicators and processes with indication of confidence intervals and accuracy;
• - development of methods for interpretation and analysis of the obtained results.

The work on the information and mathematical support of the forecasting system deserves special attention. The process of creating software can be represented as the following steps:

• - development of a methodology for structural identification of the object of forecasting;
• - development of methods for parametric identification of the forecasting object;
• - development of methods for predicting trends;
• - development of methods for predicting the harmonic components of processes;
• - development of methods for assessing the characteristics of random components of processes;
• - creation of complex models for predicting indicators that form an interconnected system.

The creation of a forecasting system requires an integrated approach to solving the problem of its information support, which is usually understood as a set of initial data used to obtain forecasts, as well as methods, methods and tools that ensure the collection, accumulation, storage, search and transmission of data in the process of functioning of the forecasting system. and its interaction with other enterprise management systems.

Information support of the system usually includes:

• - information fund (database);
• - sources of formation of the information fund, flows and methods of data receipt;
• - methods of accumulation, storage, updating and retrieval of data that form the information fund;
• - methods, principles and rules of data circulation in the system;
• - methods for ensuring the reliability of data at all stages of their collection and processing;
• - methods information analysis and synthesis;
• - ways of unambiguous formalized description of economic data.

Thus, the following main components are required to implement the forecasting process:

• - sources of internal information, which is based on management and accounting systems;
• - sources of external information;
• - specialized software that implements forecasting algorithms and analysis of results.

Given the importance of solving the problem of forecasting for market participants, it is advisable to check the quality of the proposed methods and algorithms, as well as technologies in general, using specially selected (test) initial data. A similar verification method has been used for a long time in assessing the adequacy of mathematical tools designed for nonlinear optimization, for example, using the Rosenbrock and Powell functions.

Confirmation (or verification) of the quality and performance of the forecasting technology is usually carried out by comparing a priori known model data with their predicted values ​​and evaluating the statistical characteristics of forecast accuracy. Let's consider this trick in a situation where the process models are an additive set of the trend Tt, seasonal (harmonic) and random components.

At present, a wide variety of software tools have become widespread, providing, to one degree or another, the collection and analytical processing of information. Some of them, such as MS Excel, are equipped with built-in statistical functions and programming tools. Others, especially inexpensive accounting and management accounting, do not possess such capabilities or analytical capabilities are not implemented in them sufficiently, and sometimes incorrectly. However, this is, unfortunately, inherent in some more powerful and multifunctional enterprise management systems. This situation is apparently explained by a shallow analysis on the part of the developers of the properties of the forecasting algorithms they have chosen and their uncritical application. For example, judging by the available sources, zero-order exponential smoothing is often used as the basis for predictive algorithms. However, this approach is valid only in the absence of a trend in the process under study. In fact, economic processes are non-stationary, and forecasting involves the use of more complex models than models with a constant trend.

It is interesting to trace the path of development of domestic automated banking systems from the perspective of the topic under consideration. The first banking systems were based on rigid technology, constantly requiring changes or additional software. This prompted developers of financial software, following the principles of openness, scalability and flexibility, to use industrial DBMS. However, by themselves, these DBMS turned out to be unsuitable for solving high-level analytical problems, which include the problem of forecasting. To do this, it was necessary to use additional technologies for data storage and operational analytical processing, which ensured the operation of decision support systems for financial and credit institutions and for making forecasts. The same approach is used in complex systems enterprise management.

Another direction of modern applied use of forecasting methods based on IT is the solution of a wide range of marketing tasks. An illustration is the SAS Churn Management Solution for Telecommunications software. It is intended for telecommunications operators and allows, as its developers claim, to build predictive models and use them to assess the likelihood of an outflow of certain categories of customers. The basis of this software is the Scalable Performance Data Server distributed database server, tools for building and administering data warehouses and data marts, tools intellectual analysis Enterprise Miner data, SAS/MDDB Server decision support system, and support tools.

To ensure the competitiveness of newfangled CRM-systems in the list of their advanced features, as well as for automated banking systems, reporting functions are included that use OLAP technologies and allow to some extent predict the results of marketing, sales and customer service.

There are many specialized software products, providing statistical processing of numerical data, including individual forecasting elements. These products include SPSS, Statistica, etc. These tools have both advantages and disadvantages, which significantly limit the scope of their practical application. It should be noted here that the assessment of the suitability of specialized mathematical and statistical software tools for solving forecasting problems by ordinary users who do not have special training requires a separate serious study and discussion.

However, solving forecasting problems for consumers from small and medium-sized businesses with the help of powerful and expensive information systems and technologies is practically impossible, primarily for financial reasons. Therefore, a very promising direction is the development of the analytical capabilities of existing and widespread low-cost accounting and management accounting systems. Developed additional reports based on specific business processes and containing the necessary analytical information for a particular user have a high ratio of "efficiency - cost".

Some software developers create entire lines of analytical tools. For example, Parus Corporation offers Parus-Analytics and Triumph-Analytics solutions for a wide range of users from small and medium-sized businesses. More complex tasks of analytical processing of forecast information are integrated into the Parus system in the form of a so-called situational center. According to Dmitry Sudarev, manager for the development of circulation solutions, it was decided to develop and implement software products that allow moving from simple accounting of facts in the enterprise's activities to information analysis. At the same time, a transition was planned from automating the work of accountants and middle managers to processing information for top management. Taking into account the possible circle of consumers, Parus-Analytics and Triumph-Analytics do not impose special requirements on the software and hardware environment, however, the Triumph-Analytics solution is implemented on the basis of MS SQL Server, which provides it with greater opportunities for predicting the processes under study , in particular, the harmonic component of forecasts is taken into account.

The value of the forecast increases many times when it is directly used in the management of the enterprise. Therefore, an important direction is the integration of predictive systems with systems such as Kasatka, MS Project Expert, etc. For example, the Kasatka software from SBI is positioned as an automated workplace the head and specialists of the marketing department and is intended for the development of complexes of management, marketing and strategic planning. Such a purpose predetermines the need to identify long-term trends and take them into account in planning. The forecasting horizon is determined based on the relevant goals of the organization.

Different enterprises have their own requirements for creating a budget. These features are taken into account by the creators of software products. Consider the most famous and common software products.

Hyper Pillar is a large and advanced system that fully automates budgeting. To get started, you enter planned costs and projected receipts. The result of the calculations is a dynamic company model with models responsible for each level and a simple technology for making changes to it. Hyper Pillar is well integrated with other company products: Enterprise, Essbase OLAP Server, Reporting.

Corporate Planner is a budgeting program based on a company's structural cost tree. Tree nodes - planned, actual values ​​and deviations between them. Nodes are linked by formulas. Files can be imported via ODBC. Corporate Planner is used in small companies and does not support the possibility of distributed work.

Adaytum Planning - is a three-dimensional spreadsheet with the functions of building various slices. The tables contain various data (time, finances, etc.) of each department of the company. There is a function to roll up the consolidated budget for a selected date. Adaytum Planning is an economical product for creating a small budget by applying a range of analytical tools.

Nephrite is a software product designed for use in large corporations with a holding structure. It occupies an intermediate position between computer and paper processing of documentation and has a convenient budget approval procedure. The program works even with insufficiently prepared data. The initial data are the budgets of the holding units, which should be consolidated into one holding budget. "Jade" was created on the basis of spreadsheets.

"Red Director" is a budgeting system designed for small and medium enterprises and has a simple interface. The basis of the program is a database without the possibility of integration with other software products.

Planning is a special type of scientific and practical activity, consisting in the development of strategic decisions (in the form of forecasts, projects, programs, plans), providing for the promotion of such goals and strategies for the behavior of control objects, the implementation of which ensures their effective functioning in the long term, rapid adaptation to changing external conditions.

The Project Expert program of Pro-Invest-Consulting allows users to solve the following tasks:

describe in detail and design the activities of any enterprise, taking into account changes in parameters external environment(inflation, taxes, exchange rates);

develop a plan for the development of an enterprise or the implementation of an investment project, a marketing strategy and a production strategy that ensures rational use material, human and financial resources;

determine the scheme of financing of the enterprise;

· test various scenarios development of the enterprise, varying the values ​​of factors that can affect its financial results;

prepare financial reports (report on the movement Money, balance sheet, income statement, income statement) and business plan of the investment project, fully compliant with international requirements, in Russian and English;

conduct a comprehensive analysis of the enterprise (project), including an analysis of the overall effectiveness, sensitivity analysis, analysis cash flows for each project participant, analysis financial condition and profitability of the enterprise with the help of three dozen automatically calculated indicators.

The special exchange module Project Expert allows you to import and export information in *.txt and *.dbf formats. Summary table data and textual information are freely copied via the Windows clipboard to Word, Excel and other Windows applications. Project Expert also communicates with the most famous planning and management systems: MS Project, Primavera, Project Planner and Sure Truck. Data is imported and exported in the GANTT network format, with a description of the stages, their relationships, and so on.

Being the core of the software package financial analysis and design, Project Expert is able to automatically "upload" information characterizing the starting state of the enterprise from the Audit Expert financial analysis program, and marketing operational plan data from the Marketing Expert program.

The Project Expert program comes in two versions: Base and Professional. Project Expert Professional provides its users with two additional features:

1) Update of data and control over the implementation of the project (plan). As the project progresses, the user has the opportunity to enter actual data for all project modules and calculate the updated indicators of real cash flow, as well as control the discrepancy between the real and planned Cash flow.

2) Working with a group of projects. The special module Project Integrator allows you to combine several projects (enterprises) into a group and calculate integrated performance indicators for the group as a whole, as well as compare different options for one project for any indicators.

The Biz Planner program by Pro-Invest-Consulting is a modification of Project Expert and is intended for planning and analyzing the effectiveness of investments in small and medium-sized businesses.

The Audit Expert program of Pro-Invest-Consulting is effective tool complex analysis financial condition and results of the enterprise. Bringing financial statements to international standards allows you to convert financial statements of enterprises for different years into analytical tables that meet the requirements international standards accounting.

The Marketing Expert program of Pro-Invest-Consulting is a decision support system at all stages of the development of strategic and tactical marketing plans and control over their implementation.

The Forecast Expert program by Pro-Invest-Consulting is a universal applied forecasting system and is designed to build a time series forecast using an autoregression model and an integrated moving average (ARISS, ARIMA, Box-Jenkins). Forecast Expert allows you to analyze existing data and build a forecast with boundaries confidence interval for a period of time not exceeding the observation period of the original series. The model determines the degree of influence of seasonal factors and takes them into account when building a forecast.

Microsoft's MS Project program is a development in the field of management investment projects based on graph theory and network planning.

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A study was made of the main directions and problems of implementation in practical activities organizations of modern information and communication technologies. The problems and directions of creating a unified information space. An analysis of the conditions and prerequisites was carried out practical simulation, the features of the phased construction of predictive models of the activities of organizations are analyzed. Dana a brief description of features of the use of various forecasting models, emphasis is placed on the importance of checking the adequacy of forecasting models. A review of modern information and analytical technologies for forecasting the activities of organizations has been made. Recommendations are given on the use in practice of the results of forecasting the key indicators of the organization.

information and analytical technologies

activity modeling

model adequacy analysis

forecasting the organization's activities

1. Golichev V.D., Golicheva N.D., Gusarova O.M. and others. Land of Smolensk and its population (Historical and statistical review in figures and facts). - Smolensk: Smolgortypography, 2013. - 152 p.

2. Gusarova O.M. Modeling as a way of planning and managing business results // Successes of modern natural science. - 2014. - No. 11. - P. 88–92.

3. Gusarova O.M. Modeling in managerial decision-making // Science and education: problems and development prospects: collection scientific papers based on the materials of the International scientific-practical conference. - Tambov: Yukom, 2014. - S. 41-42.

4. Gusarova O.M. Problems of integration of the theory and practice of modeling business results // Economics and education: Challenges and search for solutions: a collection of scientific papers based on the materials of the II All-Russian (correspondence) scientific and practical conference (Yaroslavl, April 15, 2014) - Yaroslavl: Chancellor, 2014. - pp. 78–82.

5. Gusarova O.M. Assessment of the relationship between regional indicators of socio-economic development (on the materials of the Central federal district Russia) // Contemporary Issues science and education. -2013. - No. 6. (Electronic journal).

6. Gusarova O.M., Zhuravleva M.A. Analysis and improvement of activities joint-stock companies// Modern science-intensive technologies. - 2014. - No. 7–3. – P. 10–12.

7. Gusarova O.M. Methods and models for predicting the activities of corporate systems // Theoretical and applied issues of education and science: a collection of scientific papers based on the materials of the International Scientific and Practical Conference. – Tambov: Yukom, 2014. – P. 48–49.

8. Gusarova O.M. Computer technologies for modeling socio-economic processes // Economic growth and competitiveness of Russia: trends, problems and strategic priorities: collection scientific articles based on the materials of the International scientific-practical conference. – M.: Unity-Dana, 2012. – S. 102–104.

9. Gusarova O.M. Study of the quality of short-term forecasting models for financial and economic indicators. – M.: 1999. – 198 p.

10. Orlova I.V., Turundaevsky V.B. Multivariate statistical analysis in the study of economic processes. Monograph. – M.: MESI, 2014. – P. 190.

In the context of the introduction of economic sanctions, a number of Russian enterprises are looking for effective ways to ensure the competitiveness of their products and improve the efficiency of the organization. In difficult economic conditions, it is necessary to use not only practical experience in organizing a business in a certain field of activity, but also modern approaches to business planning. The widespread introduction of information and analytical technologies for modeling and forecasting key business indicators into the practice of activities allows for operational monitoring of business results and the formation of an organization's development strategy. The use of information and analytical technologies allows you to create integrated business results management systems, optimize material and financial flows, minimize the costs of financial and economic activities, maximize the company's profits and solve a number of other problems.

The processes of informatization of modern society and the processes of implementation of information and communication technologies in all areas of business that are closely related to them are characterized by the massive spread of information and analytical technologies for analyzing the activities of organizations. various areas and forms of ownership. Modern information technologies make it possible to automate a number of the following areas: studying the properties of a system (object), monitoring the dynamics of development of key indicators in all areas of business, optimizing the parameters of the functioning system, creating integrated systems for monitoring and managing the system, planning and forecasting the prospects for the development of the organization.

The strategic goal of introducing information and communication technologies into all spheres of activity of modern society is to create a single information space designed to solve a wide range of issues related to access to unified databases, prompt provision of statistical reporting, creation of integrated monitoring systems for various activities. All this contributes to the creation of fundamentally new opportunities for the development of cognitive creative activity of a person: research, organizational and managerial, expert, entrepreneurial, etc. The creation of a single information space helps to improve the efficiency and quality of monitoring the activities of organizations, intensify scientific research in various areas, reduce the processing time and provide information, the efficiency and effectiveness of system management, the integration of national information system in international systems access to information resources in the field of science, culture, business and other fields of activity.

The introduction of information and communication technologies into the practical activities of organizations is characterized by a number of areas and problems:

● the technical equipment of organizations with information and communication technologies implies access to modern software and is constrained by organizational and economic factors. So access to "small informatization" is in some cases ineffective, and to "big" - expensive and does not give a quick return.

● The training of specialists in the field of information and communication technologies, especially in the field of network technologies, should become a priority task, on the solution of which the effectiveness of the organization's activities in this direction depends. A highly skilled IT professional can sometimes do the workload of an entire department of an organization. In this regard, it is necessary to work educational organizations increasingly introduce disciplines related to information technology and increase their practical orientation. Modern system education should focus on the fundamentalization of education at all its levels, the widespread use of innovative education methods and technologies, improving the quality and accessibility of education through the development of a system distance education and equipment educational process modern information and communication technologies.

● Creation information bases data in all areas of the organization's activities requires some effort, but is an important link in the integration information technologies organizations into a single information space.

One of the topical areas for introducing information and analytical technologies into the practical activities of organizations is the operational monitoring of key business indicators and forecasting alternative options for the development of the company. In the general case, the following sequence of stages in predicting the development of a research system (object) can be distinguished.

● Setting goals and objectives of the study determines the strategic guidelines and tactical directions in the study of the system, which in the course of the study can be refined and concretized.

● The formulation of the conceptual model of the system involves the examination of the system in order to identify its properties, features of dynamics and interrelationships with factors of the external and internal environment. Collection statistical information about the characteristics of the system involves further formulation of the verbal-descriptive model of the system, subject to clarification and formalization. The formulation of the conceptual model of the system presupposes a list of main questions formulated in terms of a given area of ​​study that meet the objectives of the study, and a set of hypotheses regarding the properties and characteristics of the modeling object.

● Formalization of a verbal descriptive model implies the construction of a mathematical model and the numerical determination of its parameters. An important point at the same time is right choice methods for determining the parameters of the mathematical model. Each system has its own characteristics of development, and such a characteristic of the model as adequacy, i.e. correspondence of the formalized model to the features of real processes that characterize the dynamics of the research system. Depending on the specifics of the research system, various classes of forecasting models can be preliminarily selected, for example, growth curves that characterize the dynamics of the system over time, econometric models that establish and evaluate the relationship between various internal characteristics of the system and a number of external factors, varieties of adaptive models used for highly dynamic systems with seasonal and cyclical fluctuations, from the simplest to autoregressive models with autocorrelated and heteroscedastic residuals.

● Obtaining and interpreting simulation results involve checking a number of properties of the mathematical model, in particular, checking the adequacy and accuracy of the model. The adequacy of the model characterizes the degree of closeness of the characteristics of the constructed model to the characteristics and properties of a real object (system). For a number of reasons, such as a number of assumptions that take place in modeling, the impossibility of taking into account many factors that determine the dynamics of the development of the object of study, a number of technical errors at the stage of model formalization and a number of other points, naturally lead to a difference in the characteristics of the model and the real object . It is important that these differences are not of a fundamental nature and are within certain limits (deviations). The value of permissible deviations is determined by the characteristics of the dynamics of the research system, the period of analysis of the characteristics of the system, as well as the purpose of the study. Model accuracy indicators, such as the standard deviation of a series of residuals, the average approximation error, the average relative error, characterize the degree of approximation of the simulated data to the actual observations obtained as a result of collecting statistical information. At this stage, the refinement and final choice of the model used in the future to build the forecast is carried out. At the same time, an extended verification of the adequacy of the model is carried out, including, in addition to testing hypotheses about the fulfillment of a number of statistical properties of the residual component, such as independence, randomness, the equality of the mathematical expectation of the residuals to zero, the fulfillment of the normal distribution law, the evaluation of a number of such characteristics of the model as the coefficient of determination characterizing the share of variation of the studied trait under the influence of external and internal factors, Fisher's coefficient, which evaluates the statistical significance of the resulting model. Based on the results of comparing the characteristics of adequacy and accuracy, the final choice is made predictive model.

● Building on a formalized model of forecasts and using the results of modeling in system management involves obtaining point forecasts that characterize the prospects for the development of the research system. In addition to these, interval forecasts can be constructed that carry a higher probability of obtaining intervals in which the characteristics of the system may fluctuate. It should be noted that forecasting is of a probabilistic nature and will be reliable only if the same patterns of development operate in the lead-up period that took place at the stage of system research.

The use of forecasting results in making managerial decisions is a creative process and requires not only theoretical knowledge in certain area, but also practical experience on working with the research system. At the moment, scientific research has advanced far in the development of information and analytical technologies for predicting the activities of organizations. For example, technologies of neural network forecasting, fuzzy logic, a number of specialized multifunctional analysis and forecasting programs, such as Statistica, SPSS, Stadia, VSTAT, Project Exspert and a number of other software products are known. For operational monitoring and forecasting of the results of the system functioning, as well as in educational purposes the MS Excel package can also be used, which implements trend and regression analysis, as well as allowing, on the basis of a spreadsheet processor, to calculate a number of additional characteristics of the system.

Based on the results of the study of the management system (object) using information and analytical forecasting technologies, recommendations can be formulated for improving the activities of the organization (system), for example, focusing on achieving certain values ​​of key performance indicators that implement the organization’s development strategy, optimizing cash flows, developing new promising areas of activity. The use of modern information and analytical technologies for modeling and forecasting will help to increase the efficiency of activities in the light of the implementation of the strategy and tactics of the organization's development.

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I have been doing time series forecasting for over 5 years. Last year I defended my dissertation on the topic " Time Series Forecasting Model from Maximum Similarity Sample”, however, after the defense, there were quite a few questions left. Here is one of them - general classification of forecasting methods and models.

Usually, in the works of both domestic and English-speaking authors, they do not ask themselves the question of the classification of forecasting methods and models, but simply list them. But it seems to me that today this area has grown and expanded so much that, even if the most general, classification is necessary. Below is my own version of the general classification.

What is the difference between a forecasting method and a model?

Prediction Method represents a sequence of actions that need to be performed to obtain a forecasting model. By analogy with cooking, a method is a sequence of actions according to which a dish is prepared - that is, a forecast is made.

Prediction Model is a functional representation that adequately describes the process under study and is the basis for obtaining its future values. In the same culinary analogy, the model has a list of ingredients and their ratio, which is necessary for our dish - a forecast.

The combination of method and model form a complete recipe!

It is now customary to use English abbreviations for the names of both models and methods. For example, there is the famous autoregression integrated moving average extended (ARIMAX) forecasting model. This model and its corresponding method are usually called ARIMAX, and sometimes the Box-Jenkins model (method) after the authors.

First we classify the methods

If you look closely, it quickly becomes clear that the concept of " forecasting method"much broader concept" predictive model". In this regard, at the first stage of classification, methods are usually divided into two groups: intuitive and formalized.

If we recall our culinary analogy, then even there we can divide all recipes into formalized ones, that is, written down by the number of ingredients and the method of preparation, and intuitive, that is, not recorded anywhere and obtained from the experience of the culinary specialist. When do we not use a prescription? When the dish is very simple: fry potatoes or boil dumplings, you don’t need a recipe. When else do we not use the recipe? When we want to invent something new!

Intuitive forecasting methods deal with the judgments and assessments of experts. To date, they are often used in marketing, economics, politics, since the system, the behavior of which must be predicted, is either very complex and cannot be described mathematically, or very simple and does not need such a description. Details on such methods can be found in .

Formalized Methods- forecasting methods described in the literature, as a result of which forecasting models are built, that is, they determine such a mathematical dependence that allows you to calculate the future value of the process, that is, make a forecast.

On this, the general classification of forecasting methods, in my opinion, can be completed.

Next, we make a general classification of models

Here it is necessary to proceed to the classification of forecasting models. At the first stage, the models should be divided into two groups: domain models and time series models.

Domain Models- such mathematical forecasting models, for the construction of which the laws of the subject area are used. For example, a model used to make a weather forecast contains the equations of fluid dynamics and thermodynamics. The forecast of population development is made on a model built on a differential equation. The prediction of the blood sugar level of a person with diabetes is made on the basis of a system of differential equations. In short, such models use dependencies that are specific to a particular subject area. These types of models are individual approach in developing.

Time series models- mathematical forecasting models that seek to find the dependence of the future value on the past within the process itself and calculate the forecast on this dependence. These models are universal for various subject areas, that is, they general form does not change depending on the nature of the time series. We can use neural networks to predict air temperature, and then apply a similar model on neural networks to predict stock indices. These are generalized models, like boiling water, into which if you throw a product, it will boil, regardless of its nature.

Classifying time series models

It seems to me that to compose general classification domain models is not possible: how many areas, so many models! However, time series models lend themselves easily to simple division. Time series models can be divided into two groups: statistical and structural.

AT statistical models the dependence of the future value on the past is given in the form of some equation. These include:

1. regression models (linear regression, non-linear regression);
2. autoregressive models (ARIMAX, GARCH, ARDLM);
3. exponential smoothing model;
4. model based on the maximum similarity sample;
5. etc.

AT structural models the dependence of the future value on the past is given in the form of a certain structure and rules for moving along it. These include:

1. neural network models;
2. models based on Markov chains;
3. models based on classification-regression trees;
4. etc.

For both groups, I have indicated the main, that is, the most common and detailed forecasting models. However, today there are already a huge number of time series forecasting models, and for making forecasts, for example, SVM (support vector machine) models, GA (genetic algorithm) models, and many others have begun to be used.

General classification

Thus we got the following classification of models and forecasting methods.

1. Tikhonov E.E. Forecasting in market conditions. Nevinnomyssk, 2006. 221 p.
2. Armstrong J.S. Forecasting for Marketing // Quantitative Methods in Marketing. London: International Thompson Business Press, 1999, pp. 92–119.
3. Jingfei Yang M. Sc. Power System Short-term Load Forecasting: Thesis for Ph.d degree. Germany, Darmstadt, Elektrotechnik und Informationstechnik der Technischen Universitat, 2006. 139 p.

UPD. 11/15/2016.
Gentlemen, it has reached insanity! Recently, I was sent an article for the VAK edition with a link to this entry for review. I draw your attention to the fact that neither in diplomas, nor in articles, and even more so in dissertations can't link to the blog! If you want a link use this one: Chuchueva I.A. MODEL OF PREDICTION OF TIME SERIES ON THE SELECTION OF THE MAXIMUM SIMILARITY, dissertation… cand. those. Sciences / Moscow State Technical University. N.E. Bauman. Moscow, 2012.

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