Multivariate Time Series Search
2018
multivariate time series mts are ubiquitous and are generated in areas as disparate as sensor recordings in aerospace systems music and video streams medical monitoring and financial systems domain experts are often interested in searching for interesting multivariate patterns from these mts databases which can contain up to several gigabytes of data surprisingly research on mts search is very limited most existing work only supports queries with the same length of data or queries on a fixed set of variables in this paper we propose an efficient and flexible subsequence search framework for massive mts databases that for the first time enables querying on any subset of variables with arbitrary time delays between them we propose two provably correct algorithms to solve this problem 1 an r tree based search rbs which uses minimum bounding rectangles mbr to organize the subsequences and 2 a list based search lbs algorithm which uses sorted lists for indexing we demonstrate the performance of these algorithms using two large mts databases from the aviation domain each containing several millions of observations both these tests show that our algorithms have very high prune rates 95 thus needing actual disk access for only less than 5 of the observations to the best of our knowledge this is the first flexible mts search algorithm capable of subsequence search on any subset of variables moreover mts subsequence search has never been attempted on datasets of the size we have used in this paper