Streaming pattern discovery in multiple time-series

In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Time-series). Given n numerical data streams, all of whose values we observe at each time tick t, SPIRIT can incrementally nd correlations and hidden variables, which summarise the key trends in the entire stream collection. It can do this quickly, with no bu ering of stream values and without comparing pairs of streams. Moreover, it is any-time, single pass, and it dynamically detects changes. The discovered trends can also be used to immediately spot potential anomalies, to do e cient forecasting and, more generally, to dramatically simplify further data processing. Our experimental evaluation and case studies show that SPIRIT can incrementally capture correlations and discover trends, e ciently and e ectively.