Abstract: A method for time series anomaly detection with rare event failure prediction includes determining, by a device including a processor and using a first machine learning model, a location of a data anomaly within sequential data. The method also includes determining, by the device and using a second machine learning model that is not the first machine learning model, whether a probability of a future failure event is at least a threshold probability based on the data anomaly and the sequential data.

Abstract: The technology described herein describes training an automatic semi-supervised labeler model, such as including a time series transformer with self-attention encoder, in conjunction with classifier training to produce more precise labels describing when anomalous, rare events occurred. The automatic labeler assigns a probability distribution parameterized by distribution parameters over a sample window. A classifier outputs an approximation of distribution parameters for an imprecise label (secondary event) correlated with the anomalous event. The approximation distribution along with the secondary event distribution are input into a loss function, which couples the automatic labeler to the classifier in a feedback loop. The loss function is optimized over iterations of the loop, with the loss minimized when the automatic labeler outputs the correct label. Once trained, additional labels can be automatically generated for further training.

Abstract: A system, method, and computer-readable medium for determining a root cause for a failure event. A failure of a product or system triggers a failure event. When the failure event is triggered, querying by one or more ML/DL path root cause engines is performed on stored failure incident data sets. The queried failure incident data sets are listed and ranked. Based on the ranking, a root cause is determined as to the failure event.