document: Detecting time series motifs under uniform scaling Time series motifs are approximately repeated patterns foundwithin the data . Such motifs have utility for many data mining algorithms , including rule-discovery , novelty-detection , summarization and clustering . Since the formalization of the problem and the introduction of efficient linear time algorithms , motif discovery has been successfully applied tomany domains , including medicine , motion capture , robotics and meteorology . In this work we show that most previous applications of time series motifs have been severely limited by the definition 's brittleness to even slight changes of uniform scaling , the speed at which the patterns develop . We introduce a new algorithm that allows discovery of time series motifs with invariance to uniform scaling , and show that it produces objectively superior results in several important domains . Apart from being more general than all other motifdiscovery algorithms , a further contribution of our work isthat it is simpler than previous approaches , in particular we have drastically reduced the number of parameters that need to be specified .
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