Perception Based Patterns in Time Series Data Mining

Import of intelligent features to systems supporting human decisions in problems related with analysis of time series data bases is a promising research field. Such systems should be able to operate with fuzzy perception-based information about time moments and time intervals; about time series values, trends and shapes; about associations between time series and time series patterns, etc., to formalize human knowledge, to simulate human reasoning and to reply on human questions. The chapter discusses methods developed in TSDM to describe linguistic perception-based patterns in time series databases. The survey considers different approaches to description of such patterns which use sign of derivatives, scaling of trends and shapes, linguistic interpretation of patterns obtained as result of clustering, a grammar for generation of complex patterns from shape primitives, and temporal relations between patterns. These descriptions can be extended by using fuzzy granulation of time series patterns to make them more adequate to perceptions used in human reasoning. Several approaches to relate linguistic descriptions of experts with automatically generated texts of summaries and linguistic forecasts are considered. Finally, we discuss the role of perception-based time series data mining and computing with words and perceptions in construction of intelligent systems that use expert knowledge and decision making procedures in time series data base domains.