Abstract
We consider the problem of capturing correlations and finding hidden variables corresponding to trends on collections of time series streams. Our proposed method, SPIRIT, can incrementally find 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.
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Papadimitriou, S., Sun, J., Faloutsos, C. (2007). Dimensionality Reduction and Forecasting on Streams. In: Aggarwal, C.C. (eds) Data Streams. Advances in Database Systems, vol 31. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-47534-9_12
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DOI: https://doi.org/10.1007/978-0-387-47534-9_12
Publisher Name: Springer, Boston, MA
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