发表机构
Auburn University（奥本大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有时间序列预测适配方法无法适配输入时间序列动态的局限，提出AdaCast框架，通过生成器生成特定输入的低秩参数更新，在6个基准测试中优于静态基线并提升零样本泛化能力。
AI 中文摘要
时间序列基础模型（TSFMs）在各领域均取得了优异的预测性能，但多数适配方法仍为静态的。现有“一体化”方法仅学习一组数据集级别的参数更新，将同一适配模型应用于所有输入，无法使模型参数适配每个输入时间序列的时间模式、季节性与动态变化，导致难以生成适配异构输入的预测结果。为解决该局限，本文提出AdaCast，一种用于时间序列预测的条件参数生成框架。AdaCast使用生成器为冻结的预训练TSFM生成特定输入的低秩参数更新，在训练与推理阶段均能使模型适配每个输入。在6个公开基准测试中，AdaCast在域内预测任务中始终优于静态适配基线，且提升了对跨域保留数据集的零样本泛化能力。这些结果表明，条件参数生成是实现自适应预测的有效方法。
英文摘要
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.
