Transformer-based automatic labeler for misaligned anomalous event with time series data

A method, comprising:

• inputting, by a system comprising a processor, time series data into a time series transformer with self-attention to obtain a candidate parametric distribution with respect to an anomalous event that occurred within a sampling window within the time series data;
• obtaining, by the system via a machine learning model, first distribution parameters of an imprecise label received at an unpredictable time after occurrence of the anomalous event and second distribution parameters comprising an approximation of the imprecise label;
• training, by the system, the machine learning model and the time series transformer with self-attention, comprising coupling the machine learning model to the time series transformer with self-attention via a loss function that, in a training loop, varies parameter data of the time series transformer with self-attention to reduce a loss value between the first distribution parameters and the second distribution parameters, as a result of which the loss value is reduced to a value that satisfies sufficiency criterion when the time series transformer with self-attention is trained with a set of parameter data to produce a training label that represents an actual time of the occurrence of the anomalous event;
• and
• using, by the system, the training label as part of training data to train a prediction model to predict a future anomalous event.

A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, the operations comprising:

• training an automatic semi-supervised labeler model in conjunction with training a machine learning model, the training comprising:
• generating, via the automatic semi-supervised labeler model, a first probability distribution representative of first distribution parameters representing a timeframe during which an anomalous event occurred within time series data;
• obtaining, via a machine learning model, a second probability distribution representative of second distribution parameters of the anomalous event;
• obtaining, via the machine learning model, a third probability distribution representative of an imprecise label correlated with the anomalous event;
• and
• adjusting, in a feedback loop based on a defined criterion and a loss function, the first distribution parameters of the automatic semi-supervised labeler model to obtain adjusted first distribution parameters, wherein the adjusting of the first distribution parameters in the feedback loop reduces a loss value representing a difference between the second probability distribution and the third probability distribution until the defined criterion is satisfied by the adjusted first distribution parameters.