Perception based time series data mining with MAP transform

Import of intelligent features to time series analysis including the possibility of operating with linguistic information, reasoning and replying on intelligent queries is the prospective direction of development of such systems. The paper proposes novel methods of perception based time series data mining using perceptual patterns, fuzzy rules and linguistic descriptions. The methods of perception based forecasting using perceptual trends and moving approximation (MAP) transform are discussed. The first method uses perception based function for modeling qualitative forecasting given by expert judgments. The second method uses MAP transform and measure of local trend associations for description of perceptual pattern corresponding to the region of forecasting. Finally, the method of generation of association rules for multivariate time series based on MAP and fuzzy trends is discussed. Multivariate time series are considered as description of system dynamics. In this case association rules can be considered as relationships between system elements additional to spatial, causal etc. relations existing in the system. The proposed methods are illustrated on examples of artificial and real time series.