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Time series discords are a useful primitive for time series anomalydetection, and the matrix profile is capable of capturing discord effectively.There exist many research efforts to improve the scalability of discorddiscovery with respect to the length of time series. However, there issurprisingly little work focused on reducing the time complexity of matrixprofile computation associated with dimensionality of a multidimensional timeseries. In this work, we propose a sketch for discord mining amongmulti-dimensional time series. After an initial pre-processing of the sketch asfast as reading the data, the discord mining has runtime independent of thedimensionality of the original data. On several real world examples from watertreatment and transportation, the proposed algorithm improves the throughput byat least an order of magnitude (50X) and only has minimal impact on the qualityof the approximated solution. Additionally, the proposed method can handle thedynamic addition or deletion of dimensions inconsequential overhead. Thisallows a data analyst to consider "what-if" scenarios in real time whileexploring the data.

Sketching Multidimensional Time Series for Fast Discord Mining