DISC: Domain-Integrated Similarity Cost for Time-Series Dataset

The paper introduces DISC, a Domain-Integrated Similarity Cost framework that learns to aggregate multiple similarity metrics for time‑series datasets. By calibrating on transfer observations, DISC selects, weights, and combines metrics to better predict downstream forecasting performance than any single metric. The authors demonstrate that task‑dependent similarity estimation improves alignment with actual transfer behavior in out‑of‑distribution and transfer‑learning scenarios.

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