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.
International Conference on Learning Representations , year=
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MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
STAIR's three-stage training enables simple temporal models to match or exceed complex baselines on long-term forecasting benchmarks by combining shared learning, individual adaptation, and residual cross-variable modeling.
NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
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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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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Hedging Memory Horizons for Non-Stationary Prediction via Online Aggregation
MELO aggregates base predictors and their multi-scale EWLS adaptations using MLpol to achieve oracle inequalities against best fixed and time-varying predictors in non-stationary settings.
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Three-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecasting
STAIR's three-stage training enables simple temporal models to match or exceed complex baselines on long-term forecasting benchmarks by combining shared learning, individual adaptation, and residual cross-variable modeling.
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NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
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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.