Using machine learning methods to search for the given behaviour forms of time series based on symbol aggregated approximation
In this paper, the application of the algorithm of symbolic aggregated approximation in the problems of searching for given time series behaviour forms is considered. The problem is regarded for the processing of statistical data of import / export of fish products. Modern decision support systems and strategic planning are not conceivable without the use of intelligent analysis modules based on machine learning. The problem concerns as the task of intellectual analysis of time series. The algorithm of symbolic aggregated approximation refers to a group of algorithms of structurally-linguistic analysis. A description of the algorithm of symbolic aggregated approximation and the approach to its use in time series classification problems is given. The proposed algorithm has several advantages for the search of typical forms of behaviour, which are specified semantically in the form of a local behaviour description of the time series with large variability. Approaches to the choice of parameters of algorithms are shown. As an application investigated the data published by the Organization for Economic Cooperation and Development (www.oecd.org), which represent a series of annual statistics (1995-2016). Examples are given of the application of the proposed approaches to the search for countries whose import / export is determined in advance by the given pattern, as well as the search for countries that have similar behaviour, for example. The proposed algorithm can be used for a wide class of similar tasks and is implemented in decision support systems.