Google patents method to find oddities in complex data streams

1. A computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations comprising: receiving a time series anomaly detection query from a user, the time series anomaly detection query requesting the data processing hardware to determine one or more anomalies in a set of multivariate time series data values, the set of multivariate time series data values comprising an endogenous variable and at least one exogenous variable; determining an impact of the at least one exogenous variable on the endogenous variable; determining, using the impact of the at least one exogenous variable on the endogenous variable, a set of univariate time series data values; training one or more models using each time series data value in the set of univariate time series data values; for each respective time series data value in the set of univariate time series data values: determining, using the trained one or more models and the impact of the at least one exogenous variable on the endogenous variable, an expected data value for the respective time series data value; and determining a difference between the expected data value for the respective time series data value and the respective time series data value; and for a particular time series data value in the set of univariate time series data values: determining that the difference between the expected data value for the particular time series data value and the particular time series data value satisfies a threshold; in response to determining that the difference between the expected data value for the particular time series data value and the particular time series data value satisfies the threshold, determining that the particular time series data value is anomalous; and reporting the anomalous particular time series data value to the user.