Fuzzy Knowledge Representation for Linguistic Description of Time Series

Abstract

The linguistic description of data intends to provide texts that convey the most important information contained in the data. One of the main tasks to be carried out in order to build a linguistic description is the extraction and representation of the knowledge to be transmitted. To perform this task, adequate mechanisms for knowledge representation are needed. In this paper we focus on time series data and analyze three knowledge representation languages that arise in the field of Fuzzy Sets Theory, particularly Computing with Words and Perceptions: the use of Protoforms, the Granular Linguistic Model of a Phenomenon and, specially, the use of Fuzzy Temporal Knowledge Representation Models.