Proposes a matured-ground-truth TTA protocol and Frequency-Aware Calibration (FAC) that achieves competitive performance with substantially fewer parameters than prior TSF-TTA adapters.
The Twelfth International Conference on Learning Representations , year=
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 6years
2026 6verdicts
UNVERDICTED 6representative citing papers
MS-FLOW uses a capacity-limited sparse routing mechanism to model only critical inter-variable dependencies in time series data, achieving state-of-the-art accuracy on 12 benchmarks with fewer but more reliable connections.
ACT disentangles temporal scales in stock sequences and purifies structural relations in graphs to achieve state-of-the-art cross-sectional stock ranking on CSI300 and CSI500 with up to 74.25% improvement.
UEC-STD is an architecture-agnostic corrector that uses seasonal-trend decomposition to mitigate autoregressive error accumulation in deep forecasters and reports gains across 4 backbones and 10 datasets.
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.
A nested spatio-temporal forecasting model constructs coherent regions with spectral clustering and uses progressive coarse-to-fine prediction to integrate future macro trends for improved fine-grained forecasts.
citing papers explorer
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Towards Principled Test-Time Adaptation for Time Series Forecasting
Proposes a matured-ground-truth TTA protocol and Frequency-Aware Calibration (FAC) that achieves competitive performance with substantially fewer parameters than prior TSF-TTA adapters.
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What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
MS-FLOW uses a capacity-limited sparse routing mechanism to model only critical inter-variable dependencies in time series data, achieving state-of-the-art accuracy on 12 benchmarks with fewer but more reliable connections.
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ACT: Anti-Crosstalk Learning for Cross-Sectional Stock Ranking via Temporal Disentanglement and Structural Purification
ACT disentangles temporal scales in stock sequences and purifies structural relations in graphs to achieve state-of-the-art cross-sectional stock ranking on CSI300 and CSI500 with up to 74.25% improvement.
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Reviving Error Correction in Modern Deep Time-Series Forecasting
UEC-STD is an architecture-agnostic corrector that uses seasonal-trend decomposition to mitigate autoregressive error accumulation in deep forecasters and reports gains across 4 backbones and 10 datasets.
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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.
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Nested Spatio-Temporal Time Series Forecasting
A nested spatio-temporal forecasting model constructs coherent regions with spectral clustering and uses progressive coarse-to-fine prediction to integrate future macro trends for improved fine-grained forecasts.