![]() ![]() #Software tanaka future hd seriesIf time series are not cointegrated, then the estimation of their dependency makes no sense. To estimate this dependency the regression model has to be identified and to built the adequate regression model the cointegration between TS has to be studied. ![]() In this approach it is supposed that variation of one time series causes the variation of another time series. In The second approach to Time series forecasting the predictive model includes another Time series valuesin addition. ![]() In this approach regression models on time and autoregression models are used to predict values and global trends. ![]() This approach is widely spread, and many methods and modelshave been proposed: statistical, fuzzy ,and their combination. The first one is based on forecasting only one Time series. There are two approaches to Time series forecasting and analysis. #Software tanaka future hd softwareThe regular analysis of the trend and dependenciesin technologies, scientific achievements and user requirement, expressed in time series, allows to uncover ways to create a new useful ideas for software development. Unfortunately, the trend forecasting, as a data miningformal tool, for developing of new software and for reengineering the present software practically isn't used. Particularly the forecasting ofĮconomic indicators and its trends is a part of planning process in the enterprise. The results of Time series forecasting and its trends are useful for business and management. The core of the predicative analytics undoubtedly is the time series analysis of the importantsoftware parameters.įorecasting is one of the problems of Time series analysis. The predicative analytics is the effective tool in the analysis of trends, if we want to create new competitive software. So, to fulfilled such researchfor development of the competitive software the data mining algorithms, extracting new trends and predicative analytics have to be used. This research should be directed on the perspective functions and software technologies, scientific achievements and user requirement. To develop not only new, but competitive software, it is necessary to research of appearing trendsby using formal methods at an analysis stage. Predicative analytics is the major formal tool for developing of new software and for reengineering the present software. Keywords-data mining, development, software, fuzzy time series, forecasting, fuzzy tendency The proposed algorithm was examined experimentally and showed the efficiency The proposed algorithm expands the opportunities of time series short-term forecasting on the base of fuzzy trends, as the historical software time series are of small length. This algorithm takes into account the dependence of the current state of time series from the previous one, the influence of basic fuzzy projected trends in the time series. We propose the algorithm adjustments of the time series forecasting. Nadej da Yarushkina Dept.of Information systems Ulyanovsk state technical University Ulyanovsk, Russia Afanasieva Dept.of Information systems Ulyanovsk state technical University Ulyanovsk, Russia Timina Dept.of Information systems Ulyanovsk state technical University Ulyanovsk, Russia article is devoted to the problem of applying the formal data mining tool - forecasting - for the developing of new software and for reengineering the present software. Predicative analytics for developing software ![]()
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