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Forecasting with Sparse but Informative Variables: A Case Study in Predicting Blood Glucose
When forecasting blood glucose, for example, intrinsic effects can be inferred from the history of the target signal alone (\textit{i. e.} blood glucose), but accurately modeling the impact of extrinsic effects requires auxiliary signals, like the amount of carbohydrates ingested.
Learning Through Limited Self-Supervision: Improving Time-Series Classification Without Additional Data via Auxiliary Tasks
We explore a novel self-supervision framework for time-series data, in which multiple auxiliary tasks (e. g., forecasting) are included to improve overall performance on a sequence-level target task without additional training data.
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