In this paper, we introduce SPIRIT (Streaming Pattern dIscoveRy in multIple Timeseries).

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 buffering 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 efficient 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, efficiently and

effectively.