document: iSAX : indexing and mining terabyte sized time series Current research in indexing and mining time series data has produced many interesting algorithms and representations . However , the algorithms and the size of data considered have generally not been representative of the increasingly massive datasets encountered in science , engineering , and business domains . In this work , we show how a novel multi-resolution symbolic representation can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature . Our approach allows both fast exact search and ultra fast approximate search . We show how to exploit the combination of both types of search as sub-routines in data mining algorithms , allowing for the exact mining of truly massive real world datasets , containing millions of time series .
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