Segmented Shape-Symbolic Time Series Representation

Abstract

Abstract. This paper introduces a symbolic time series representation using monotonic sub-sequences and bottom up segmentation. The representation min-imizes the square error between the segments and their monotonic approximations. The representation can robustly classify the direction of a segment and is scale in-variant with respect to the time and value dimensions. This paper describes two experiments. The first shows how accurately the monotonic functions are able to discriminate between different segments. The second tests how well the segmenta-tion technique recognizes segments and classifies them with correct symbols. Fi-nally this paper illustrates the new representation on real-world data.