The framework provides universal abstractions to represent models, extensibility to add new pipelines and datasets, hyperparameter standardization, pipeline verification, and frequent releases with published benchmarks.
We then propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor to incorporate the successes and address the limitations of each method.
The detection of anomalies in time series data is a critical task with many monitoring applications.
Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the physics of objects.
An estimated 180 papers focusing on deep learning and EHR were published between 2010 and 2018.
However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations.