document: Online discovery and maintenance of time series motifs The detection of repeated subsequences , time series motifs , is a problem which has been shown to have great utility for several higher-level data mining algorithms , including classification , clustering , segmentation , forecasting , and rule discovery . In recent years there has been significant research effort spent on efficiently discovering these motifs in static offline databases . However , for many domains , the inherent streaming nature of time series demands online discovery and maintenance of time series motifs . In this paper , we develop the first online motif discovery algorithm which monitors and maintains motifs exactly in real time over the most recent history of a stream . Our algorithm has a worst-case update time which is linear to the window size and is extendible to maintain more complex pattern structures . In contrast , the current offline algorithms either need significant update time or require very costly pre-processing steps which online algorithms simply can not afford . Our core ideas allow useful extensions of our algorithm to deal with arbitrary data rates and discovering multidimensional motifs . We demonstrate the utility of our algorithms with a variety of case studies in the domains of robotics , acoustic monitoring and online compression .
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