Introduces the task of counterfactual time series forecasting with textual conditions plus a text-attribution mechanism that improves accuracy by distinguishing mutable from immutable factors.
The Thirty-Fifth
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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UNVERDICTED 2representative citing papers
KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.
citing papers explorer
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What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions
Introduces the task of counterfactual time series forecasting with textual conditions plus a text-attribution mechanism that improves accuracy by distinguishing mutable from immutable factors.
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Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting
KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.