arXiv：cs.AI· Darahaas Nallagatla, Darryl Cherian Jacob, Pan He·· 3 小时前
AdaCast：面向自适应时间序列预测的条件参数生成框架
AdaCast: Conditional Parameter Generation for Adaptive Time Series Forecasting
AI 导读
AdaCast 通过生成器为冻结的预训练时间序列基础模型（TSFM）生成输入特定的低秩参数更新，并在训练和推理阶段逐输入自适应调整模型。在六个公开基准上，AdaCast 持续优于静态适配基线，并提升跨领域留出数据集的零样本泛化能力。
正文
Abstract:Time-series foundation models (TSFMs) have achieved strong forecasting performance across domains. However, most adaptation methods remain static. Existing all-in-one methods learn a single set of dataset-level parameter updates and apply the same adapted model to every input. As a result, they cannot adapt the model parameters to the temporal patterns, seasonality and dynamics of each input time series. This limits their ability to produce forecasts that are tailored to heterogeneous inputs. To address this limitation, we propose AdaCast, a conditional parameter generation framework for time-series forecasting. AdaCast uses a generator to produce input-specific low-rank parameter updates for a frozen pretrained TSFM. These updates adapt the model to each input during both training and inference. Across six public benchmarks, AdaCast consistently outperforms static adaptation baseline in in-domain forecasting and improves zero-shot generalization to held-out datasets across domains. These results demonstrate that conditional parameter generation provides an effective approach for adaptive forecasting.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.12240 [cs.LG] |
| (or arXiv:2610.12240v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12240 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pan He [view email]
[v1]
Thu, 8 Oct 2026 16:21:19 UTC (3,501 KB)
来源：arXiv：cs.AI · arxiv.org