A study was carried out of the main directions and problems of introducing modern information and communication technologies into the practical activities of organizations. Problems and directions for creating a unified information space are identified. An analysis of the conditions and prerequisites for practical modeling was carried out, and the features of the stage-by-stage construction of forecast models of organizations' activities were analyzed. A brief description of the features of using various forecasting models is given, and 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 was carried out. Recommendations are given for using the results of forecasting key indicators of an organization in practice.

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. The Smolensk Land and its population (Historical and statistical review in figures and facts). – Smolensk: Smolgortypography, 2013. – 152 p.

2. Gusarova O.M. Modeling as a way to plan and manage business results // Advances in modern science. – 2014. – No. 11. – P. 88–92.

3. Gusarova O.M. Modeling in management decision making // Science and education: problems and development prospects: collection of scientific papers based on the materials of the International Scientific and Practical Conference. – Tambov: Ukom, 2014. – pp. 41–42.

4. Gusarova O.M. Problems of integration of theory and practice of modeling business results // Economics and education: Challenges and search for solutions: 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 (based on materials from the Central Federal District of Russia) // Modern problems of science and education. –2013. – No. 6. (Electronic magazine).

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

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

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

9. Gusarova O.M. Study of the quality of short-term models for forecasting 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 searching for effective ways to ensure the competitiveness of their products and increase the efficiency of the organization. In difficult economic conditions, it is necessary to make decisions using not only practical experience in organizing a business in a certain field of activity, but also modern approaches to planning the activities of an enterprise. The widespread introduction into practice of information and analytical technologies for modeling and forecasting key business indicators makes it possible to quickly monitor business results and formulate an organization’s development strategy. The use of information and analytical technologies allows you to create integrated systems for managing business results, 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 closely related processes of introducing information and communication technologies into all areas of business are characterized by the massive spread of information and analytical technologies for analyzing the activities of organizations in various spheres and forms of ownership. Modern information technologies make it possible to automate a number of the following areas: researching the properties of a system (object), monitoring the dynamics of development of key indicators of all areas of business, optimizing the parameters of the operating 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 the creation of a unified information space designed to solve a wide range of issues related to access to unified databases, prompt provision of statistical reporting, and the creation of integrated monitoring systems for various areas of activity. All this contributes to the creation of fundamentally new opportunities for the development of human cognitive creative activity: research, organizational and managerial, expert, entrepreneurial, etc. The creation of a unified information space helps to increase the efficiency and quality of monitoring the activities of organizations, intensify scientific research in various areas, reduce the processing time and provision of information, the efficiency and effectiveness of system management, the integration of the national information system into international systems of access to information resources in the field of science, culture, and business and other areas 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 limited by organizational and economic factors. Thus, access to “small informatization” is in some cases ineffective, and access to “large” is expensive and does not give a quick return.

● Training of specialists in the field of information and communication technologies, especially in the field of network technologies, should become a priority task, the solution of which determines the effectiveness of the organization’s activities in this direction. A highly qualified IT specialist can sometimes complete the work of an entire department of an organization. In this regard, it is necessary to increasingly introduce disciplines related to information technology into the activities of educational organizations and increase their practical orientation. The modern education system should focus on the fundamentalization of education at all levels, the widespread use of methods and technologies of innovative education, improving the quality and accessibility of education through the development of a distance education system and equipping the educational process with modern information and communication technologies.

● Creating information databases for all areas of an organization’s activities requires some effort, but is an important link in the integration of an organization’s information technologies into a single information space.

One of the current 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 general, we can distinguish the following sequence of stages in predicting the development of a research system (object).

● Setting the goals and objectives of the study determines strategic guidelines and tactical directions in the study of the system, which can be clarified and specified during the research process.

● The formulation of a conceptual model of a system involves examining the system in order to identify its properties, dynamics and relationships with factors of the external and internal environment. The collection of statistical information about the characteristics of the system presupposes the further formulation of a verbal descriptive model of the system, subject to clarification and formalization. The formulation of a conceptual model of a system presupposes a list of basic questions formulated in terms of a given area of ​​research 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 in this regard is the correct choice of methods for determining the parameters of a mathematical model. Each system is characterized by its own development features, and such characteristics of the model as adequacy, i.e., largely depend on the choice of method for numerical determination of model parameters. compliance of the formalized model with the features of real processes characterizing 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, types of adaptive models used for highly dynamic systems with seasonal and cyclical fluctuations, from the simplest to autoregressive models with autocorrelated and heteroskedastic residuals.

● Obtaining and interpreting modeling results involves 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 the real object (system). Due to a number of reasons, such as a number of assumptions that take place during 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 formalizing the model and a number of other points, naturally lead to differences in the characteristics of the model and the real object . It is important that these differences are not fundamental and are within certain limits (deviations). The magnitude of permissible deviations is determined by the characteristics of the dynamics of the research system, the period of analysis of the system characteristics, as well as the purpose of the research. Indicators of model accuracy, such as the standard deviation of a number of residuals, the average error of approximation, and 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 selection of the model used in the future to build a forecast is carried out. In this case, an extended check 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, equality of the mathematical expectation of the residuals to zero, the fulfillment of the normal distribution law, assessment of a number of such model characteristics as the coefficient of determination, characterizing the proportion of variation the studied characteristic 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 of the forecast model is made.

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

The use of forecasting results in management decision-making is a creative process and requires not only theoretical knowledge in a certain area, but also practical experience in working with the research system. At the moment, scientific research has made great progress in the development of information and analytical technologies for forecasting the activities of organizations. For example, the 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 system functioning results, as well as for educational purposes, the MS Excel package can also be used, which implements trend and regression analysis, and also allows, based on a spreadsheet processor, to calculate a number of additional system characteristics.

Based on the results of a study of a 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 improve operational efficiency in the light of the implementation of the organization's development strategy and tactics.

Bibliographic link

Salaeva Inga, Kostyunina Daria

The research work presents a historical and diagnostic picture of the quality of modern forecasting and reveals forecasting technology using Excel. The research report is presented in the attached file. Product of project activities - on the school portal

Download:

Preview:

Open International Research Conference for High School and Students “Education. The science. Profession"

Section

Information Technology

Subject

Computer technology and forecasting

Kostyunina Daria

Salaeva Inga

Educational institution

Municipal educational institution Gymnasium No. 39 “Classical”

Scientific adviser:

Osipova Svetlana Leonidovna, computer science teacher of the highest category

Otradny

Formulation of the problem.Forecast seasonal ice cream sales.

Initial data.Product sales volumes by season.

Solution algorithm.

1. Present ice cream sales data by season in table form.
2. The trend is determined, best approximating the actual data (in this problem this is a polynomial trend)

Conclusions.

The polynomial model describes the dependence more reliably, since its coefficient of determination R 2 closer to 1. The closer R 2 to unity, the more successfully the model is built.

The resulting model predicts seasonal ice cream sales well. But it is difficult to predict sales in the next seasons, since when extrapolating, it is not recommended to go far from the experimental area. However, you may notice that summer ice cream sales (especially in June and July) will be high.

1. Calculation of correlations

Dependencies between quantities, each of which is subject to completely uncontrollable scatter, are called correlation dependencies.

Task:

Formulation of the problem. To determine the dependence of the academic performance of high school students on two factors: the provision of the school library with textbooks and the provision of computers at the school.

Initial data.Results from measuring both factors in 11 different schools.

Solution algorithm.

1. Present the obtained data in the form of a table.
2. Calculate the coefficient using the correlation formula. IN Excel there is a function for this CORREL , which is part of the group statistical functions.

Conclusions.

Linear correlation coefficients were obtained for both dependencies. As can be seen from the table, the correlation between the provision of textbooks and academic performance is stronger than the correlation between computer support and academic performance. We can conclude that the book still remains a more significant source of knowledge than the computer.

1. Optimal planning

The objects of planning can be a variety of systems: the activities of an individual enterprise, an industry or agriculture, a region, and finally, a state. It could also be a health condition or weather condition. The formulation of the planning problem is as follows:

1. There are some planned indicators: x, y and others;
2. There are some resources: R1, R2 and others, through which these targets can be achieved. These resources are almost always limited;
3. there is a certain strategic goal depending on the values x, y and other planned indicators on which planning should be oriented.

It is necessary to determine the value of planned indicators, taking into account limited resources, subject to the achievement of the strategic goal. This will be the optimal plan.

conclusions

Forecasting is an integral part of any area of ​​life, such as management or economics, mathematics or meteorology.

While working on the project, we found out that high-quality forecasting of various processes of human activity is not possible without modern computer technologies. For this purpose, we studied the capabilities of the MS Excel spreadsheet processor to create computer models used in forecasting. Many human functions in management, planning, and forecasting can be transferred to a computer.

Topic 3.1. Intelligent technologies in forecasting

Now strategic management is a dominant component of the successful development of an organization in the long term. When developing a strategy, and subsequently for the successful implementation of strategic changes, the manager must conduct a thorough analysis of the internal and external environment of the organization, in particular various micro- and macroeconomic indicators, socio-economic, political and legal aspects of the development of the state and society in a given period.

The issue of obtaining analytical information, on the basis of which the parameters of an organization’s development are forecasted and a strategy is developed in a constantly changing external environment, becomes extremely relevant, and in many cases, decisive. This issue becomes especially relevant in modern conditions of informatization of society, when there is so much information and it is so diverse both in content and in the empirical aspect that obtaining the necessary information seems to be extremely complex and requires colossal labor costs of employees’ working time.

To obtain analytical information and predict the development of the internal and external environment, organizations currently use information technologies based on techniques for extracting knowledge about the objects of analysis from the general body of information.

Today there are two main types of information technologies:

1. traditional (classical) information technologies;

2. non-traditional information technologies (they are also called intelligent technologies).

Traditional information technologies are based on formal methods of knowledge extraction and formal forecasting algorithms (regression methods, statistical and econometric methods, Box-Jenkins methods, ARIMA, ARMA).

However, traditional information technologies are effective mainly at the operational level and, to a lesser extent, at the tactical management levels, where, as a rule, the analyzed information is an ordered set of relatively easily formalized data, the amount of which is small. At the level of strategic management, a manager or a group of experts, which may include top managers, planners, economists, and development department employees, as a rule, already deal with a huge amount of information from completely different areas, which exists in various forms. For example, a manager intuitively feels the consequences of a change in political course in the region, a technologist - the parameters of the production process, a planner - the dependence of indicators on one another. These are just a few factors. But the problem is that there are so many factors and information influencing production that in real practice many factors are discarded. This inevitably leads to inaccuracies and errors, causing the strategy trajectory to deviate from its shortest distance. This, in turn, leads to increased costs and decreased financial results. Thus, adequate consideration of the largest number of factors and information can give the organization a significant economic effect.

It is precisely in cases of difficult to formalize information, insufficient empirical data, a large number of variables with uncertainty and multifactorial processes occurring in a constantly changing external environment that intelligent information technologies are used, which are based on the concept of intellectualization of analysis and forecasting processes.

Intellectualization means the transfer of human organization and thinking techniques into the technical field.

It can be said that intelligent technologies are superior to traditional software and hardware technologies in the case of those tasks in which a person with his characteristic developed thinking is superior to them.

At the moment, there are four main types of intelligent information technologies:

1. Expert systems (fuzzy logic).

2. Genetic algorithms.

3. Nonlinear dynamics (chaos theory).

4. Artificial neural networks.

Expert systems based on fuzzy logic use intuitive-empirical models of the functioning of an organization, compiled by an expert or a group of experts in the form of rules of conditional logical inference like “If., Then.” and form a knowledge base on the basis of which the system makes this or that decision. For example, in conditions of uncertainty of information about the quantity of products produced, recommend to the manager, based on data on market conditions and the introduced withdrawal rules, to produce a larger volume of products. Significant disadvantages of such systems are: the subjective nature of the rules set by the expert, and the great difficulty in changing the rules of conditional logical inference when the external environment changes.

Intelligent information technologies based on genetic algorithms and selection principles better adapt to changing environmental conditions, but the process of their creation is extremely complex, and in the real operating conditions of an enterprise it is problematic to find a specialist in this field, which equally applies to complex nonlinear dynamics.

The optimal artificial intelligence technology intended for use in the process of developing and implementing an organization’s strategy is artificial neural networks, since in principle they do not need to build a model, but build it themselves only on the basis of the information provided. That is why neural networks are already indispensable tools for effective management of an organization, where it is necessary to solve difficult-to-formalize problems in conditions of significant uncertainty in the processes taking place.

Let's look at smart technologies in more detail.

Expert systems

The implementation of expert systems is most often presented in the form of computer programs that imitate the thinking processes of an expert in a specific subject area. Examples of expert systems include both business decisions and professional tasks from medical diagnostics to oil exploration and computer system configuration27.

Expert systems are based on laboratory experiments that determine what an expert will do in a given situation and then record this knowledge as a set of rules. Expert systems separate the methods for processing information from the information itself, allowing software developers to create programs that process information in several different ways, which is useful for many types of problems.

Send your good work in the knowledge base is simple. Use the form below

Students, graduate students, young scientists who use the knowledge base in their studies and work will be very grateful to you.

Similar documents

Theoretical foundations of forecasting and its main methods, stages and types of forecasts. Methods for forecasting the business environment. Analysis of the practical use of the decision tree method in making management decisions using the example of the Chita-Spetsstroy company.

course work, added 05/05/2011


The essence of economic forecasting, characteristics of the main forms of foresight. Foresight of internal and external conditions of activity. Types of forecasts and forecasting technology. Forecasting methods: expert, statistical, combined.

course work, added 12/22/2009


Accuracy in decision making. Fundamentals of forecasting, statistical and regression analysis using software using the example of fuel sales at an automatic gas station. Trend forecasting and forecasting using the autoregression method.

course work, added 06/04/2015


Forecasting methods in developing management decisions. The role of forecasting in regional socio-economic management processes. Ways to improve planning and forecasting activities in managing the sustainable development of the region.

course work, added 04/10/2014


The process of enterprise management is the development of management decisions based on an analysis of the situation. Forecasting methods in an organization: objectives, feasibility of application, results. Expert assessments in making planning decisions, extrapolation of trends.

course work, added 03/02/2012


Evolution, concept and essence of forecasting and planning of enterprise activities. The structure of economic foresight. Distinctive features of the scenario method, as well as technological, expert, survey, and regulatory forecasting methods.

abstract, added 04/15/2011


Essence and classification of risks. Techniques for developing and selecting management decisions under risk conditions. The main characteristics of a travel company. Risk when making management decisions in the travel company LLC "Olga Romanova Travel Company".

course work, added 01/21/2014


Forecasting in innovation activities, classification of forecasts by time and scale of action. Forecasting the provision of innovations, their social and environmental consequences. Determination and assessment of the innovative potential of the project and its parameters.

course work, added 07/24/2009

• Tutorial

I have been doing time series forecasting for over 5 years. Last year I defended my dissertation on the topic “ Time series forecasting model using maximum similarity sampling“However, after the defense there were still quite a few questions left. Here is one of them - general classification of forecasting methods and models.

Typically, in both domestic and English-language works, authors do not ask the question of classifying 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 it is 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 forecasting model?

Forecasting 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.

Forecasting model there 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 ratios required for our dish - the forecast.

The combination of method and model forms a complete recipe!

Currently, it is customary to use English abbreviations for the names of both models and methods. For example, there is a famous forecasting model of autoregressive integrated moving average taking into account an external factor (auto regression integrated moving average extended, ARIMAX). 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 “ forecasting method"is much broader than the concept" forecasting model" In this regard, at the first stage of classification, methods are usually divided into two groups: intuitive and formalized.

If we remember our culinary analogy, then all recipes can be divided into formalized, that is, written down by the quantity of ingredients and method of preparation, and intuitive, that is, not written down anywhere and obtained from the experience of the cook. When do we not use a recipe? When the dish is very simple: fry potatoes or cook dumplings, a recipe is not needed. When else do we not use a recipe? When we want to invent something new!

Intuitive forecasting methods deal with the judgments and assessments of experts. Today they are often used in marketing, economics, and politics, since the system whose behavior needs to be predicted is either very complex and cannot be described mathematically, or is very simple and does not need such a description. Details about this kind of methods can be found in.

Formalized methods— forecasting methods described in the literature, as a result of which forecasting models are built, that is, a mathematical relationship is determined that allows one to calculate the future value of the process, that is, make a forecast.

In my opinion, this general classification of forecasting methods can be completed.

Next we will make a general classification of models

Here it is necessary to move on 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, the model used to make weather forecasts contains equations of fluid dynamics and thermodynamics. The population development forecast is made using a model built on a differential equation. The forecast of the blood sugar level of a person with diabetes is made based on a system of differential equations. In short, such models use dependencies specific to a specific subject area. This type of model is characterized by an individual approach to development.

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 a forecast based on this dependence. These models are universal for various subject areas, that is, their general appearance does not change depending on the nature of the time series. We can use neural networks to predict air temperature, and then use a similar model on neural networks to forecast stock indices. These are generalized models, like boiling water, into which if you throw a product, it will cook, regardless of its nature.

Classifying time series models

It seems to me that it is not possible to create a general classification of domain models: as many domains as there are, 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.

IN 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, nonlinear regression);
2. autoregressive models (ARIMAX, GARCH, ARDLM);
3. exponential smoothing model;
4. maximum similarity sampling model;
5. etc.

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

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

For both groups, I 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 the point of insanity! Recently I was sent an article for review for the VAK publication with a link to this entry. Please note that neither in diplomas, nor in articles, much less in dissertations You can't link to the blog! If you want a link, use this one: Chuchueva I.A. TIME SERIES FORECASTING MODEL BY MAXIMUM SIMILARITY SAMPLING, dissertation... Ph.D. those. Sciences / Moscow State Technical University named after. N.E. Bauman. Moscow, 2012.

Tags: Add tags