Description of multivariate time series by means of trends characterization in the fuzzy domain

This paper presents a new method to automatically obtain linguistic descriptions from a multivariate time series. The first step consists of the induction from the multivariate time series of a fuzzy model called the temporal fuzzy model. Later this fuzzy model is analyzed in order to obtain trends based on the output variable. With this information, the trends for the input variables are also obtained and together with the output variable trends are represented in such a way that the temporal evolution of the trend is stored within the own-trends. In a trend there are always some elements or points of interest that are more relevant than others for generating a relevant final description. These elements are also extracted and stored, making the final description generation process more efficient. An event search process generates the final linguistic description. The events are identified in the trends or in the structure containing the points of interest previously selected. Once an event is identified, new text is added at the end of the linguistic description. The design of the events and the text related to each event is made with the cooperation of experts in the field of application. The presented approach is checked in sports, more concretely, in countermovement jumping.