Scientific Journal of KubSAU

The article deals with methods of visual-graphic analysis (technical analysis) and a possibility of adapting them to the conditions (indicators) of the sugar subcomplex from the position of integrated production systems (IPS). It should be noted that technical analysis is very popular. Thanks to the advent of powerful processors for computers and inexpensive software, trade analysts have access to technical analysis tools. The topic is becoming increasingly relevant in connection with the high pace of the global economic community. Visual graphical analysis (technical analysis), as well as its latest methods (indicators) that are adapted to modern economic conditions, are sort of the primary "blueprints" for the more complex forecasting tools, without which none of the analyst can do. Separating statistics from mathematics as an independent unit occurred after the development and start of mass use of tools visual graphical analysis (VGA) in various applied Sciences. The main feature of the prediction is the decision of the tasks, which are implemented in the algorithm of sequential nonparametric model. This indicates the improving the validity of information when predicting performance of IPS SP AIC. For a more General (objective) picture of the forecasting activities of IPS SP you need to apply this analysis in combination with other tools, such as hierarchical analysis of structural change and of correlation and spectral analysis. According to the forecasts obtained with the help of the indicators VGA, countries such as Brazil and India over time, waiting for the "overheating" of the economy due to unprecedented growth in the volume of growing sugar cane and manufacturing raw sugar. However, it is not necessary to consider the visual-graphic analysis as a perfect tool for forecasting market trends. Technical analysis should be seen as a tool for analysis and forecasting, which uses as the basis for short-term forecasting (benchmark) for operational decision-making by managers as a major sugar holdings, and the Ministry of agriculture

The article deals with methods of visual-graphic analysis (technical analysis) and a possibility of adapting them to the conditions (indicators) of the sugar subcomplex from the position of integrated production systems (IPS). It should be noted that technical analysis is very popular. Thanks to the advent of powerful processors for computers and inexpensive software, trade analysts have access to technical analysis tools. The topic is becoming increasingly relevant in connection with the high pace of the global economic community. Visual graphical analysis (technical analysis), as well as its latest methods (indicators) that are adapted to modern economic conditions, are sort of the primary "blueprints" for the more complex forecasting tools, without which none of the analyst can do. Separating statistics from mathematics as an independent unit occurred after the development and start of mass use of tools visual graphical analysis (VGA) in various applied Sciences. The main feature of the prediction is the decision of the tasks which are implemented in the algorithm of sequential nonparametric model. This indicates the improving the validity of information when predicting performance of IPS SP AIC. For a more General (objective) picture of the forecasting activities of IPS SP you need to apply this analysis in combination with other tools, such as hierarchical analysis of structural change and of correlation and spectral analysis. According to the forecasts obtained with the help of indicators the VGA, countries such as Brazil and India over time, waiting for the "overheating" of the economy due to unprecedented growth in the volume of growing sugar cane and manufacturing raw sugar. However, it is not necessary to consider the visual-graphic analysis as a perfect tool for forecasting market trends. Technical analysis should be seen as a tool for analysis and forecasting, which uses as the basis for short-term forecasting (benchmark) for operational decision-making by managers as a major sugar holdings, and the Ministry of agriculture

The article considers brief theoretical information of the wavelet transform and the methods of identification of nonlinear time-varying systems using multi resolution wavelet transform. The methods of data processing based on wavelet transformation are widely used in recent times. Wavelets have significant advantages compared to Fourier transform because wavelet transform tells you about not only the frequency spectrum of the signal, but also on what point in time came one or another harmonic. With their help, you can easily analyze intermittent signals or signals with powerful bursts. Moreover, wavelets allow us to analyze data according to scale, on one of the preset levels (small or large). The unique properties of wavelets allow constructing a basis in which the representation of the data will be expressed with just a few nonzero coefficients. This property makes wavelets a useful tool for data packaging. Small expansion coefficients may be discarded in accordance with the selected algorithm without a significant impact on the quality of the compressed data. Wavelets have found wide application in digital signal processing and data analysis. There are two classes of wavelet transforms: continuous and discrete. In the article we have implemented the discrete wavelet transform with the resulting output distribution on a 3D graph. The algorithm and the results of converting a time series of indicators of integrated industrial systems of sugar subcomplex in agro industrial subcomplex. The methods of neural network modeling for improved accuracy in predicting high-frequency oscillation are applied in the research. The method of determination of cyclic patterns based on coefficients of the wavelet transform has been proposed

Improvement of consistency and effectiveness of strategic planning and forecasting in modern conditions requires the development of the existing classifications of types of planning, strategies, forecasts and forecast methods. This work examines the introduction to problems of spectral analysis of the macroeconomic dynamics of worldwide and Russian sugar subcomplex. The article is devoted to forecasting the activities of integrated manufacturing systems of sugar subcomplex in agro industrial subcomplex. The article deals with aspects of practical application of economic-mathematical methods (based on spectral analysis) to control the economic parameters of the integrated industrial systems of the sugar subcomplex, oriented to meet the needs of the sugar production of the people not only of separate regions, but also of the country as a whole. Procedures of identifying and research of periodic components of the dynamics of the development of the agriculture segment are based on methods of spectral analysis of random processes. The study describes the performed experiments with various kinds of non-stationary time series of agricultural sector and food industry sugar subcomplex. The article provides examples of the results of numerical experiments the spectra of time series of sugar production, sown areas, gross harvest and yield of sugar beet and sugar cane across countries, systematic ideas and methods underlying the spectral analysis. The estimation of obtained results is given in article. The author’s algorithm for the adaptive method of spectral analysis was implemented in the context of a specific software product, named MS Excel. The results of the empirical research confirmed the possibility of practical use of developed models in forecasting possible scenarios for the development of sugar subcomplex in the interests of integrated production systems. The results are illustrated by numerous graphs based on real data. The projections of latent structures of sugar subcomplex by macroregions are built. It is revealed that each of the macroeconomic time series can contain at least from 2 to 13 harmonics (cycles) of different kind and strength of impact on the trend

This article is devoted to the practical application of economic-mathematical methods (based on correlation analysis) to control the economic parameters of the integrated production systems sugar subcomplex (IPS SS) AIC oriented to meet the needs in the sugar production of the population not only of individuals, but also of the regions and the country as a whole. This article discusses and solves the following tasks: autocorrelation and partial autocorrelation functions, cross-correlation function (correlation matrix) study of deciduous macroeconomics series, with appropriate verification (test) Durbin - Watson. The study used Statistica, MS Excel and Xlstat add-in. The work describes experiments with various kinds of nonstationary time series of the agricultural sector and food industry sugar subcomplex, as well as the test results on the difficulty of communication between them. We have identified industry-high cycles. The article presents results of numerical experiments autocorrelation of the time series of sugar production, acreage, gross harvest and yield of sugar beet and sugar cane, by country. Systematically, we describe ideas and methods underlying the correlation analysis. We have given the evaluation of the results of correlation analysis on each type. Further, it can be assumed that the proposed techniques will greatly affect a key points when making recommendations for new models of production of sugar products, market-oriented – this will minimize the time and cost of the finished product that will make a more stable position in the sector for this integrated production system in relation to its competition

This article is devoted to the practical application of economic-mathematical methods (based on correlation analysis) to control the economic parameters of the integrated production systems sugar subcomplex (IPS SS) AIC oriented to meet the needs in the sugar production of the population not only of individuals, but also of the regions and the country as a whole. This article discusses and solves the following tasks: autocorrelation and partial autocorrelation functions, cross-correlation function (correlation matrix) study of deciduous macroeconomics series, with appropriate verification (test) Durbin - Watson. The study used Statistica, MS Excel and Xlstat add-in. The work describes experiments with various kinds of nonstationary time series of the agricultural sector and food industry sugar subcomplex, as well as the test results on the difficulty of communication between them. We have identified industry-high cycles. The article presents results of numerical experiments autocorrelation of the time series of sugar production, acreage, gross harvest and yield of sugar beet and sugar cane, by country. Systematically, we describe ideas and methods underlying the correlation analysis. We have given the evaluation of the results of correlation analysis on each type. Further, it can be assumed that the proposed techniques will greatly affect a key points when making recommendations for new models of production of sugar products, market-oriented – this will minimize the time and cost of the finished product that will make a more stable position in the sector for this integrated production system in relation to its competition

Objective: To improve the consistency and effectiveness of strategic planning and forecasting in modern conditions it requires development of the existing classifications of types of planning, strategies, forecasts and forecast methods. This study examines the introduction to problems of spectral analysis of the macroeconomic dynamics of key world and Russian sugar subcomplex. The article is devoted to forecasting the activities of integrated manufacturing systems of sugar subcomplex in agro industrial subcomplex. As well as to the practical application of economic-mathematical methods (based on spectral analysis) to control the economic parameters of the integrated industrial systems of the sugar subcomplex, oriented to meet the needs of the sugar production of the population not only of individuals, but of the regions and the country as a whole. Discussion: Procedures to identify and study the dynamics of periodic components of the development of the agriculture segment agriculture are based on methods of spectral analysis of random processes. The article describes the performed experiments with various kinds of non-stationary time series of agricultural sector and food industry sugar sub-complex. The article presents results of numerical experiments with the spectra of time series of sugar production, sown areas, gross harvest and yield of sugar beet and sugar cane country. Systematic ideas and methods underlying the spectral analysis were shown. The article also assesses the results. Results: The algorithm developed by the author for the adaptive method of spectral analysis was implemented by the author in the context of a specific software product, namely in MS Excel format. The results of the empirical research confirmed the possibility of practical use of developed models in forecasting likely scenarios for the development of sugar sub-complex in the interests of integrated production systems. The results are illustrated by numerous graphs based on real data. We have also built projection of latent structures of sugar subcomplex in the macroregions. It is revealed that each of the macroeconomic time series can contain at least from 2 to 9 harmonics (cycles) of different kind and strength of impact on the trend

Objective: To improve the consistency and effectiveness of strategic planning and forecasting in modern conditions it requires development of the existing classifications of types of planning, strategies, forecasts and forecast methods. This study examines the introduction to problems of spectral analysis of the macroeconomic dynamics of key world and Russian sugar subcomplex. The article is devoted to forecasting the activities of integrated manufacturing systems of sugar subcomplex in agro industrial subcomplex. As well as to the practical application of economic-mathematical methods (based on spectral analysis) to control the economic parameters of the integrated industrial systems of the sugar subcomplex, oriented to meet the needs of the sugar production of the population not only of individuals, but of the regions and the country as a whole. Discussion: Procedures to identify and study the dynamics of periodic components of the development of the agriculture segment agriculture are based on methods of spectral analysis of random processes. The article describes the performed experiments with various kinds of non-stationary time series of agricultural sector and food industry sugar subcomplex. The article presents results of numerical experiments with the spectra of time series of sugar production, sown areas, gross harvest and yield of sugar beet and sugar cane country. Systematic ideas and methods underlying the spectral analysis were shown. The article also assesses the results. Results: The algorithm developed by the author for the adaptive method of spectral analysis was implemented by the author in the context of a specific software product, namely in MS Excel format. The results of the empirical research confirmed the possibility of practical use of developed models in forecasting likely scenarios for the development of sugar sub-complex in the interests of integrated production systems. The results are illustrated by numerous graphs based on real data. We have also built projection of latent structures of sugar subcomplex in the macroregions. It is revealed that each of the macroeconomic time series can contain at least from 2 to 9 harmonics (cycles) of different kind and strength of impact on the trend

The article is devoted to the search and development of new models of structural changes. The results of these studies correct the activity of the major sugar integrated production systems of sugar subcomplex in the agro industrial subcomplex. The article reveals the problem of formation of an integrated methodology for analysis of structural changes in the economy of AIC, denoted with indicators and macroeconomic parameters of the sugar subcomplex, which need to be considered in the evaluation of structural changes. We set the task of developing a new tool of mathematical statistics, solving a range of problems for identifying non-stationary time series (NSTS) of the “beginning” of new super cycles (sets of cycles). In the economy the classic solution to this problem is in the field of detection of non-equilibrium effect of delayed reaction to earlier technological change, changes in foreign trade conditions, low mobility of labor and capital, and the various barriers to free competition. From our point of view, the ideal solution corresponds to the detection channel offset and the verification of dynamic series for homogeneity, i.e. the presence of phase transitions. The structural shift in the economy can be seen as a qualitative change in the system, consisting in the replacement of the previously existing ties between its constituent parts with new ones. Such shifts are due to the uneven development of the various elements of the economic system, they indicate that there are changes in the needs of subjects of economic life and economic resources. The author proposes a control parameter of the analysis, which uses methods to determine structural changes (tests Pettitte, Buishand and Alexandersson). The article deals with structural changes in the sugar industry of agriculture. The analyzed period is according to different categories from 60 to 180 years. The presence of structural changes is investigated by indicators such as the amount of sown areas, gross harvest, yield of sugar beet and sugar production from sugar beets and cane. We have investigated the theoretical and methodological approaches, the existing methods for the analysis of structural shifts in the economy and their impact on reproductive processes, their classification is given. We have identified key issues of improving efficiency and quality of transforming the economic structure of the sugar subcomplex. The article shows the dynamics of indicators of the economic structure of the sugar subcomplex of Russia and other countries of the world for different periods of time and its impact on sugar subcomplex of AIC. The author has proposed an adaptive algorithm and model test for homogeneity (structural shift) for integrated production systems that focus on sugar subcomplex of AIC. This method has been tested by the author in relation to economic systems (at various levels) of sugar subcomplex in agro industrial subcomplex of Russia, other countries and the world at large. Along with this, the author has proposed (we have developed a hierarchical analysis of structural changes) to use the identification of clusters for each category of sugar subcomplex with attraction of mathematical apparatus in the form of tests for homogeneity. We have marked indicators and parameters for the analysis of structural shift, the main reasons for this phenomenon. The results of empirical studies carried out have confirmed the possibility of practical use of the developed analysis

The article considers brief theoretical information of the wavelet transform and the methods of identification of nonlinear time-varying systems using multiresolution wavelet transform. The methods of data processing based on wavelet transformation are widely used in recent times. Wavelets have significant advantages compared to Fourier transform because wavelet transform tells you about not only the frequency spectrum of the signal, but also on what point in time came one or another harmonic. With their help, you can easily analyze intermittent signals or signals with powerful bursts. Moreover, wavelets allow us to analyze data according to scale, on one of the preset levels (small or large). The unique properties of wavelets allow constructing a basis in which the representation of the data will be expressed with just a few nonzero coefficients. This property makes wavelets a useful tool for data packaging. Small expansion coefficients may be discarded in accordance with the selected algorithm without a significant impact on the quality of the compressed data. Wavelets have found wide application in digital signal processing and data analysis. There are two classes of wavelet transforms: continuous and discrete. In the article implemented the discrete wavelet transform with the resulting output distribution on a 3D graph. The algorithm and the results of converting a time series of indicators of integrated industrial systems of the sugar subcomplex in the agro industrial subcomplex. The methods of neural network modeling for improved accuracy in predicting high-frequency oscillation are applied in the research. The method of determination of cyclic patterns based on coefficients of the wavelet transform is proposed