arXiv AI
FreDF: Learning to Forecast in the Frequency Domain
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Time-o1: Time-Series Forecasting Needs Transformed Label Alignment
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Channel-wise Retrieval for Multivariate Time Series Forecasting
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AdaCast: Conditional Parameter Generation for Adaptive Time Series Forecasting
AdaCast introduces a conditional parameter generation framework that tailors time‑series forecasting models to each input by producing low‑rank updates for a frozen pretrained foundation model. Unlike static adaptation methods that apply a single set of updates to all inputs, AdaCast generates input‑specific updates during both training and inference. Experiments on six public benchmarks show that AdaCast consistently outperforms static baselines in in‑domain forecasting and improves zero‑shot generalization to unseen datasets across domains.
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DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
arXiv:2510.24574v3 Announce Type: replace-cross Abstract: Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. Th...