MULTIVARIATE TIME SERIES ANOMALY DETECTION

Abstract: a method includes receiving a query to determine anomalies in a set of multivariate time series data values including an endogenous variable and an exogenous variable. the method includes determining an impact of the exogenous variable on the endogenous variable. the method includes determining a set of univariate time series data values and training one or more models using the univariate time series data values. the method includes determining an expected data value for a respective time series data value and determining a difference between the expected data value and the respective time series data value. the method includes determining that the difference between the expected data value for a particular time series data value and the particular time series data value satisfies a threshold. in response, the method includes determining that the particular time series data value is anomalous and reporting the anomalous value to a user.

Inventor(s): Yuxiang Li, Haoming Chen, Jiashang Liu, Xi Cheng

CPC Classification: G06F11/0751 (Responding to the occurrence of a fault, e.g. fault tolerance)

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