
Keyword: "time series"
Andrei Alekseevich Pertsev, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Head of an department at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of the automated enterprise management system implementation. email: mars@mv.ruA. A. Pertsev
Aleksandr Nikolaevich Podobrii, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Deputy Chief of a department at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of the automated enterprise management system implementation. email: mars@mv.ruA.N. Podobrii
Iuliia Aleksandrovna Radionova, Candidate of Sciences in Engineering; graduated from the Faculty of Mechanics and Mathematics of Ulyanovsk State University; Lead Programming Engineer at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of automated workflow systems, intelligent technical documentation storage bases and systems for statistical analysis of supplier appraisal; research interests are in the field of electronic document management, archival depositories, statistical data analysis, decision support systems. email: juliaowl@mail.ruI.A. Radionova 

Material scheduling to provide manufacturing in machine industry
The article describes an approach to the material scheduling to provide manufacturing by machinery designers. The approach is based on the statistics of material consumption over the last periods and on manufacturing sequence. The article considers information technology alternatives of production reserve planning.
The authors propose the model of material support based on a time series analysis. They describe a study scheme and a database structure for calculating by the model developed. The algorithm steps of data analysis, modeling of time series and resulted VS test values comparison are described in details. The article defines an experimental calculation to test the model validity and diagrams to compare the time series of the main and auxiliary material sample pieces. The programmed calculations are given as an additional module for interactive web resource and are implemented through TSQL.
Manufacturing plan formation, time series, critical path, scheduling network, material, resources, project manufacturing, mechanical engineering, capacity, statistics of operations fulfilled, shiftwork scheduling.



Sections: Information systems
Subjects: Information systems. 
Anton Alekseevich Romanov, Candidate of Sciences in Engineering; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Associate Professor of the Department of Information Systems of UlSTU; an author of articles in the field of intelligent systems of data storage and processing. email: romanov73@gmail.comA.A. Romanov
Aleksei Aleksandrovich Filippov, Candidate of Sciences in Engineering; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Associate Professor of the Department of Information Systems of UlSTU; an author of articles in the field of ontological modeling, intelligent systems of data storage and processing. email: al.filippov@ulstu.ruA.A. Filippov 

An approach to the contextual analysis of time series
Forecasting methods despite their conventions and limitations are the evolution of descriptive analytics mechanisms. Any model of the realworld objects works only under conditions of restrictions and agreements. The same conclusion can be made for the forecasting process, that it is not possible to forecast future state of the researched objects for 100%. However, building the most accurate forecast under the given conditions is the key.
Modern data mining methods are based on a variety of models. However, such models can’t define the components of researched objects and processes except those contained in their models.
The context allows using additional domain knowledge in describing the behavior of objects and processes in the form of qualitative assessments of their state. The same dataset in different domains will have various models and analysis results. The article deals with an approach to the domain context formation based on the ontology for analyzing time series of industrial processes indicators. The logical representation of the ontology based on the ALCHI(D) descriptive logic is also considered. The article describes as well experimental results confirming the correctness and effectiveness of the approach proposed.
Time series, time series analyzing, context, domain, ontology.



Sections: Information systems
Subjects: Information systems. 
Iuliia Aleksandrovna Radionova, Candidate of Sciences in Engineering; graduated from the Faculty of Mathematics and Mechanics of Ulyanovsk State University; finished her postgraduate study at Ulyanovsk State Technical University; Lead Programming Engineer at FRPC JSC ‘RPA ‘Mars’; an author of articles in the field of automated workflow systems, intelligent technical documentation storage bases and systems for statistical analysis of supplier appraisal; research interests are in the field of electronic document management, archival depositories, statistical data analysis, decision support systems. email: juliaowl@mail.ruI. A. Radionova
Aleksandr Leonidovich Savkin, Candidate of Military Science, Associate Professor; graduated from the Ulyanovsk Higher Military Command School of Communications and the Marshal Budjonny Military Academy of Signal Corps; completed his postgraduate studies at the Military Academy of Communications; Head of Science and Engineering Support Department of FRPC JSC ‘RPA ‘Mars’; an author of scientific works, manuals, and articles in the field of the development and modeling of computeraided control systems of communications and statistical analysis of supplier appraisal. email: mars@mv.ruA. L. Savkin


Forecast modeling of peak work loads based on time series analysis The article deals with the analysis of work load in a technical documentation department of researchandproduction association. Data for the analysis are formalized as unstructured time series (time series). Basic definitions of time series, methods for detecting anomalous data, trend criteria, trend equation types are studied. Methodology for data structuring of initial time series intended for the following analysis is developed. Modification of the criterion for anomalous point detection is proposed. All the considered algorithms are implemented; database structure is developed. Information on the department work performed during the period 2008 to 2018 was captured to carry out computing experiments. Data are structured according to the proposed methodology. Anomalous data are detected by standard and modified criteria; trend availability is identified. Conclusions are made on criteria and equations that are optimal for initial data set. Data for year 2018 are modelled based on various equations; results are compared with actual data. Conclusions were drawn up regarding the ability to be used the methods examined for the selected data set.
Time series, trend, anomalous data, work load.



Sections: Mathematical modeling
Subjects: Mathematical modeling. 
Aleksei Andreevich Sapunkov, graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Assistant Professor at the Department of Information Systems of UlSTU; an author of articles in the field of analyzing and forecasting the time series. email: sapalks@gmail.comA.A. Sapunkov
Tatiana Vasilevna Afanaseva, Doctor of Sciences in Engineering, Associate Professor; graduated from the Radioengineering Faculty of Ulyanovsk Polytechnic Institute, Professor of the Department of Information Systems of UlSTU; an author of articles in the field of analyzing and forecasting the time series. email: tv.afanasjeva@ gmail.comT.V. Afanaseva


A decision support methodology for prioritizing user requests for software modifications
In this paper, approaches are analyzed and a decision support methodology for prioritizing user requests for software modifications received through a technical support service is described. This task is relevant for iteratively developing software, since at each iteration a stream of requests from end users for software modification is received. The aim of the proposed methodology is to automate the process of evaluating and prioritizing (ranking) requests to convert them into requirements. A distinctive feature of the methodology is the inclusion in the query assessment of information about the sources of queries, as well as point and temporal estimates. To analyze the changes in the number of requests of each type, it is proposed to use their forecasting based on fuzzy time series models. The proposed methodology will reduce the time costs for managers and software developers to analyze problems and make decisions on how to fix them. The article provides a formal description of the stages of the proposed methodology and considers an example of its application as a means of supporting decisionmaking on the inclusion of highpriority requests in the list of requirements for software development. In conclusion, conclusions are drawn on the limits of applicability of the proposed methodology.
Intellectual decision support, prioritization of requirements, System analysis, developing software products, forecasting, fuzzy forecasting models, time series.



Sections: Information systems
Subjects: Information systems. 
Irina Aleksandrovna Moshkina, Ulyanovsk State Technical University, Candidate of Science in Engineering; Associate Professor at the Department of Information Systems of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [email: timina_i@mail.ru]I. Moshkina,
Evgenii Nikolaevich Egov, Ulyanovsk State Technical University, graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; Assistant at the Department of Information Systems at Ulyanovsk State Technical University; at author of articles in the field of data mining. [email: e.egov@ulstu.ru]E. Egov


Project State Forecasting on Time Series of Metrics and on Detected Anomalies
The article deals with an example of the anomalies detection when analyzing the time series of metrics that characterize the project activity to adjust the project state forecasting. Project activity metrics are analyzed. A forecasting algorithm based on fuzzy tendency of time series metrics is developed and implemented. Authors suggest a procedure for detecting the time series anomaly based on entropy. A formula for computing the entropy measure for a fuzzy time series is proposed. The algorithm allows to take into account the dependence of the predicted values on the entropy measures. For forecasting, a hypothesis is used, which is formed for a given period on the basis of a trend. The results of the application of the proposed approach for forecasting the state of the project "FreeNAS9" are given. Time series, software metrics, entropy, forecasting.



Sections: Artificial intelligence
Subjects: Artificial intelligence, Information systems. 
Iulia Evgenevna Kuvaiskova, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor; graduated from the Faculty of Economics and Mathematics of Ulyanovsk State Technical University; Associate Professor of the Department of Applied Mathematics and Informatics at Ulyanovsk State Technical University; an author of papers in the field of time series modeling and forecasting. [email: u.kuvaiskova@mail.ru]I. Kuvaiskova, Anna Aleksandrovna Aleshina, Ulyanovsk Instrument Manufacturing Design Bureau Joint Stock Company, Candidate of Engineering; graduated from the Faculty of Economics and Mathematics of Ulyanovsk State Technical University; Software Engineer at Ulyanovsk Instrument Manufacturing Design Bureau Joint Stock Company; an author of papers in the field of time series modeling and forecasting. [email: a2nia@mail.ru]A. Aleshina


The Use of Adaptive Regression Modeling in the Description and Forecasting of the Object Technical State
The safe operation of the technical object is an important task. The technical object management system often includes a subsystem for monitoring its settings, so, the object management solution is made due to its technical condition. The effectiveness of such a subsystem essentially depends on the accuracy of prediction of the object technical parameters. Therefore, it is necessary to build adequate mathematical models of the controlled object parameters and their subsequent use for the object state forecasting and, accordingly, for providing effective and operational management decisions. In order to solve the formulated problems, the article describes the algorithms of mathematical modeling and forecasting of the object technical state on the basis of the adaptive regression modeling. The algorithms allow several times increase of the prediction accuracy. High accurate results of the object state forecasting are used in the decisionmaking process related to the object management. The efficiency of the offered algorithms is investigated by the example of modeling and forecasting of the object technical state. Adaptive regression modeling, time series, forecasting, technical object.



Sections: Mathematical modeling
Subjects: Mathematical modeling. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; graduated from the Faculty of Radioengineering at Ulyanovsk State Technical University; First ViceRector  ViceRector for Scientific Affairs of Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina, Evgenii Nilolaevich Egov, Ulyanovsk State Technical University, Postgraduate Student and Assistant at the Department of Information Systems of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [email: e.egov@ulstu.ru]E. Egov


The Algorithm for Identifying New Anomalies in Technical Time Series Diagnosis
The article discusses the ways to diagnose the time series in order to detect anomalies in them. The authors propose to determine the number of each point of the values of the two parameters. Also a set of situations related to changes in the values of these parameters between the points should be prepared. While analyzing series, the frequency of each situation occurrence should be determined. If the probability of situations occurrence is less than 0.01, then such situations may be attributed to an abnormal ones. On the basis of the previous situation choice, a template that allows identifying these anomalies in the future is created. As one of the pairs for situations identification, the entropy measures values obtained from fuzzy time series are proposed to use. The first measure of entropy is calculated by the value of the membership function point compared to the fuzzy label. The second measure of entropy is calculated on the basis of the deviation of the actual value trends from the forecasting one. Series analysis is performed on the basis of the second pair. This pair represent “fuzzy label  fuzzy trend” one. This pair was introduced to identify longterm stay in the areas of certain states, which can be attributed to the abnormal ones. It also describes the algorithm to identify previously unknown anomalies and search of anomalies patterns. The experiment was carried out in order to check the efficiency of the algorithm. Time series of physical quantities characterizing work of important units of helicopter engines in which it was necessary to reveal the presence of defects were investigated. The main interest of this paper is the anomaly detection algorithm based on the measure of the uncertainty of the time series. The article is intended for professionals diagnosing technical systems. Entropy measure, diagnosis, time series, anomalies.



Sections: Information systems
Subjects: Information systems. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, First ViceRector  ViceRector for Science of Ulyanovsk State Technical University (UlSTU); graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina, Valeriia Vadimovna Voronina, Ulyanovsk State Technical University, Candidate of Engineering; graduated from the Faculty of Information Systems and Technologies at Ulyanovsk State Technical University; Associate Professor at the Department of Information Systems at Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [email: vvsh85@mail.ru]V. Voronina, Irina Aleksandrovna Timina, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University with a specialty of Applied Informatics (in Economics); an author of articles in the field of intellectual analysis of time series. [email: timina_i@mail.ru@ulstu.ru]I. Timina, Evgenii Nikolaevich Egov, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series. [email: e.egov@ulstu.ru]E. Egov


Forecasting Technical System State With the Application of Entropy Measure for Fuzzy Time Series Diagnosis
This article discusses the ways to forecast time series of technical systems on the basis of the hypothesis of trends conservation, the hypothesis of trends stability and the hypothesis of forecasting for a specified period as well as forecasting with the use of the measure of entropy for fuzzy time series. The method of calculating the measure of entropy for fuzzy time series has been described in the previous issue of the journal. The software system of diagnosing and forecasting fuzzy time series based on the measure of entropy is also considered in the article. The system is divided into several modules, with the opportunity to use some of them in the other systems of time series prediction. The main interest of this paper is the prediction algorithm that was designed on the basis of time series measure of entropy and the comparison of the two approaches to forecasting fuzzy time series. The comparison was made on the basis of the values of MAPE, MSE, RMSE errors obtained from values of 10 rows predicted by two programs. The first program is based on the selection of one of the hypotheses, the second one described in this article is based on the prediction with the use of measure of entropy. This article is intended for professionals diagnosing technical systems. Measure of entropy, prediction, time series.



Sections: Mathematical modeling
Subjects: Mathematical modeling, Automated control systems. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, First ViceRector  ViceRector for Science of Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University ; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina, Irina Aleksandrovna Timina, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of UlSTU with a specialty of Applied Informatics (in Economics); an author of articles in the field of data mining. [email: timina_i@mail.ru@ulstu.ru]I. Timina


Automated System Model and Control Tools on the Base of Program Code Metrics History
The article discusses the issue of project management associated with the development of software products through using automated version control system (VCS) and the analysis of program code metrics. This problem is solved through studying VCS functioning with the further use of the data analysis component of the project management based on the application of the time series (TS) model, the construction of fuzzy TS trends, clustering for dominant fuzzy trends separation, extracting time series predicate, the similarity measure of time series, their correlation, prediction and correction of the forecast. Time series of the number of errors in the total number of changes, the number of improvements in the same number of changes, the number of new functions were used as program code metrics. The hypothesis of the trend permanency was chosen for prediction. The given approach was examined on the examples. Version control system, time series, fuzzy trend, forecasting, forecast adjustment.



Sections: Computeraided engineering
Subjects: Computeraided engineering, Automated control systems. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, First ViceRector  ViceRector for Science of Ulyanovsk State Technical University; graduated from the Faculty of Radioengineering of Ulyanovsk State Technical University ; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina, Valeriia Vadimovna Voronina, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intellectual analysis of time series [email: vvsh85@mail.ru]V. Voronina, Evgenii Nilolaevich Egov, Ulyanovsk State Technical University, Assistant at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technologies of Ulyanovsk State Technical University; an author of articles in the field of intelligent information systems. [email: e.egov@ulstu.ru]E. Egov


Entropy Application to the Diagnosis of Technical Time Series
The article deals with the method for time series diagnosis based on the measure of the time series uncertainty. The formula for finding the measure of entropy for fuzzy time series is determined. The algorithm for finding the measure of entropy for fuzzy time series is of particular interest. A model of expert diagnostic rules for aircraft accessories is developed. The models of the behavior of objects such as the main gearbox and power plant engine helicopter are offered. Interpretation of natural experiment for the purpose of diagnosis of helicopter units held by analyzing the quality of the built models. A set of programs for mathematical modeling and predicting the behavior of aircraft accessories based on fuzzy measure of the uncertainty of the time series is developed. The model showed high accuracy in determining the characteristics of the time series and the identification of dangerous areas while experimenting. The developed algorithm can be successfully applied for the diagnosis and prediction of time series. This article is intended for specialists diagnosing technical systems. Measure of entropy, diagnosis, time series.



Sections: Information systems
Subjects: Information systems. 
Tatyana Vasilievna Afanasieva, Ulyanovsk State Technical University, Doctor of Engineering; graduated from the Radio Engineering Faculty of Ulyanovsk Polytechnic Institute; Professor of Information System Department in Ulyanovsk State Technical University; an author of books and articles in the field of time series data mining and analysis. [email: tv.afanasjeva@gmail.com]T. Afanasieva


Forecasting Timeseries Local Trends in the Big Data Analysis
According to the forecasts in the field of IT (IDC) the growth of the stored data volumes obtained from various sources will be doubling every two years until 2020. This tendency will remain stable in the conditions of the data increase generated in the OLTP systems, on social networks and by devices in case of mutual exchange of information during the intensive development of the data warehouse, "cloud computing", "the Internet of things" and "digital production" technologies. All that gives rise to a considerable interest in the Big Data analysis and processing from both business and a scientific community. The technology of analytical OLAPsystems for the analysis of the large data, focused on providing a visualization of the multidimensional data and the formation of interactive reports, is one of the most popular in decision support and Business intelligence systems. A promising technology in the field of analysis in addition to the OLAPsystems and aiming at identifying hidden patterns in the large data, is a technology of time series data mining. The most important tasks of time series data mining certainly should include forecasting time series points. A scientific basis of the methodology of the local trends forecasting as fuzzy trends for univariate time series (numeric and nonnumeric) which lead to the fuzzy time series are discussed in this article. The solution to this problem is interesting both in theoretical and practical aspects. It is known that fuzzy time series models are proven themselves for a short length time series, and for a long length time series a large computation is required. This paper proposes a ktrend algorithm for extracting local trends out of the big data, considered as a time series, and a time series model in terms of fuzzy local trends is shown. An effect of applying the proposed approach, expressed in a significant reduction of the computational cost when using a fuzzy time series model is shown. Big data, fuzzy models, time series, local trends, forecasting.



Sections: Mathematical modeling
Subjects: Mathematical modeling, Automated control systems, Artificial intelligence. 
Vadim Sergeevich Moshkin, Ulyanovsk State Technical University, Postgraduate Student at the Department of Information Systems at Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technology of Ulyanovsk State Technical University; an author of articles in the field of intelligent systems. [email: postforvadim@yandex.ru]V. Moshkin, Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor, Head of the Department of Information Systems at Ulyanovsk State Technical University; an author of more than 250 papers in the field of soft computing, fuzzy logic, and hybrid systems. [email: jng@ulstu.ru]N. Yarushkina


Ontological Timeseries Analysis System
This article describes a semantic approach to analyzing the time series as an example of local area network (LAN) status parameters using the ontology of problem area. We represent a formal model of the OWLontology for the considered subject domain, an ontological view model for a set of production rules. An inference algorithm for LAN architecture modification during its status estimation while artificially increasing traffic is proposed.We solved the aggregation problem of the different approaches to expert knowledge representation through the product knowledge integration into the ontological model using SWRL rules. In addition, the implementation of this algorithm in the timeseries analysis software TSAnalyzer is considered.The results of computational experiments on LANstatus simulation while artificially increasing traffic as an example of the LAN of the Center for Development of Electronic Media Technologies at Ulyanovsk State Technical University are represented. We summarized the research results conducted and evaluated further research findings expectation in this domain. Ontology, time series, data mining, semantics.



Sections: Artificial intelligence
Subjects: Artificial intelligence, Automated control systems. 
Iulia Evgenevna Kuvaiskova, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor of the Department of Applied Mathematics and Informatics at Ulyanovsk State Technical University; graduated from the Economics and Mathematics Faculty of Ulyanovsk State Technical University with a specialty in Applied mathematics; an author of papers in the field of time series modeling and forecasting. [email: u.kuvaiskova@mail.ru]I. Kuvaiskova, Anna Aleksandrovna Aleshina, Postgraduate student at the Department of Applied Mathematics and Informatics of Ulyanovsk State Technical University; graduated from the Economics and Mathematics Faculty of Ulyanovsk State Technical University with a specialty in Applied mathematics; an author of papers in the field of time series modeling and forecasting, [email: a2nia@mail.ru]A. Aleshina


Increasing the Efficiency of Technical Object Control System With the Use of Adaptive Dynamic Regression Modeling of the Time Series
The authors suggest using a software package of early warning emergency situations in automatic control systems. This package is based on forecasting of interdependent time series of a management object state controlled characteristics. The system performance effectiveness is proved by estimating the probability of erroneous decision acceptance and the availability factor by means of the hydroelectric unit example. Emergency situation, early warning, information and mathematical system, time series, probability of erroneous decisionmaking, availability factor.



Sections: Software and mathematical support of computers, computer systems and networks
Subjects: Information systems. 
Anton Alexeevich Romanov, Ulyanovsk State Technical University, postgraduate student at the Chair of Information Systems of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technology of Ulyanovsk State
Technical University; author of articles in the field of intellectual systems for data storage and processing [email: romanov73@gmail.com]A. Romanov


TimeSeries Modeling and Forecasting Based on FTransformation
Method
The paper describes a forecast method for components of vector trend, an algorithm of Ftransform for time series, solving
of remainders, inverse Ftransform, reconstruction of time series based on forecasted trends, as well as analyses the method
application. Ftransform, forecast, time series.



Sections: Mathematical modeling, calculi of approximations and software systems
Subjects: Mathematical modeling. 
Tatiana Vasilyevna Afanasyeva, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor at the Chair 'Applied Mathematics and Information Science' of Ulyanovsk State Technical University; author of articles, a monograph, a textbook in the field of intellectual analysis of time series. [email: tv.afanaseva@mail.ru]T. Afanasyeva, Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; ProRector for Science; head of the Chair 'Information Systems' at Ulyanovsk State Technical University; author of articles and monographs in the field of intellectual analysis of data. [email: jng@ulstu.ru]N. Yarushkina


Efficiency Analysis of Fuzzy Trend Model for Forecasting of Time Series
The article describes a new model for the analysis of time series for the forecast of small time series. The base of the new model is formalization and identification of a new object of time series  a fuzzy trend. The suggested model does not have assumptions used in stochastic simulation, and is easy for the implementation and developed for linguistic interpretation of results. The experiment studies of the accuracy figures of the suggested model reveal its adequacy for forecasting of small time series and competitiveness in comparison with its analogues. Forecasting, time series, fuzzy trend, accuracy figures.



Sections: Artificialintelligence systems
Subjects: Artificial intelligence, Automated control systems, Mathematical modeling. 
Nadezhda Glebovna Yarushkina, Ulyanovsk State Technical University, Doctor of Engineering, Professor; ProRector for Science; head of the Chair 'Information Systems' at Ulyanovsk State Technical University; author of articles and monographs in the field of intellectual analysis of data. [email: jng@ulstu.ru]N. Yarushkina, Valeria Vadimovna Voronina, Ulyanovsk State Technical University, Postgraduate student at the Chair 'Information Systems' of Ulyanovsk State Technical University; graduated from the Faculty of Information Systems and Technology of Ulyanovsk State Technical University; author of articles in the field of intellectual systems for storage and processing of data. [email: vvsh85@mail.ru]V. Voronina, Tatiana Vasilyevna Afanasyeva, Ulyanovsk State Technical University, Candidate of Engineering, Associate Professor at the Chair 'Applied Mathematics and Information Science' of Ulyanovsk State Technical University; author of articles, a monograph in the field of intellectual analysis of data. [email: tv.afanaseva@mail.ru]T. Afanasyeva


Diagnostics of Helicopter Nodes on Basis of a Model of Grained Time Series
In the present paper the authors consider a solution to the problem of diagnostics of helicopter nodes. The diagnostics is carried out by analyzing time series of key physical quantities, based on an expert rulebase containing statements on the significance of trends of change of these variables. In the paper, some expert rules for the helicopter nodes such as a helicopter propulsion engine and main gearbox, are also folmulated. Diagnostics, time series, helicopters, expert rulebase.



Sections: Artificialintelligence systems
Subjects: Artificial intelligence, Information systems. 

Solution of Timeseries Datamining Tasks Within Structural and Linguistic Approach
The article gives a description of a new structural and linguistic approach intended for the implementation
of timeseries datamining. The orientation of this approach to the analysis of time series of different length
and extraction of knowledge on behaviour of time series in the form of fuzzy elementary tendencies as
well as the presentation of the results in the linguistic form in bounded natural language, allows extending
the group of potential users of systems implemented using the principles of structural and linguistic
approach. Data mining, time series, fuzzy tendency, knowledge extraction, structural and linguisticapproach, modeling.



Sections: Theoretical issues of automation of command and control processes
Subjects: Artificial intelligence, Information systems. 

Integral Method of Fuzzy Modeling and Analysis of Fuzzy Tendencies
The article deals with a new method of modeling of time series, which integrates intellectual methods of
task solution concerning knowledge extraction from time series not only in numerical form but also in the
form of linguistic description of levels and elementary tendencies. Fuzzy model, time series, fuzzy tendency, knowledge extraction, forecast.



Sections: Theoretical issues of automation of command and control processes
Subjects: Artificial intelligence. 
