Multivariate Time Series Dataset which contains Anomalies of Multiple Types

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To the best of our knowledge, there is no existing public dataset which contains enough annotated anomaly samples to enable a thorough empirical evaluation for the proposed MTLED. Thus, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with the help of the simulation tools (Agots-master  and DeepADoTS-master ). These simulation tools take the correlation of variables into consideration and all the labels needed can be acquired easily without expert priors. In Agots-master, 4 typical sensor-level anomaly types are defined: ‘point’, ‘shift’, ‘variance’, ‘trend’. Point anomalies are single values that noticeably deviate from their adjacent values, shift anomalies are sequences that noticeably deviate from their surrounding sequences, variance anomalies are sequences that oscillates within a large range, and trend anomalies are sequences which show an obvious trend of ascending or descending. The severity of the generated anomalies can be controlled by the parameter in the simulation program. Besides that, other anomaly types such as the sinusoidal waves with white noises, and other harmonic signals are generated by DeepADoTS-master, and all the other anomalies except the above 4 typical types are labeled as ‘other’ class. 
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