发表机构
Deakin University（迪肯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出SteerCast，一种无需更新参数的基于检索的潜在引导方法，在推理时通过聚合训练集近邻的引导向量注入预测器，提升仅解码器时间序列预测的准确性。
AI 中文摘要
时间序列预测旨在从历史观测值和辅助特征中预测未来值。我们提出了SteerCast，一种基于检索的潜在引导方法，可在推理时改进仅解码器预测器，无需更新其参数。SteerCast通过存储每个历史窗口的表示以及在预测器潜在空间中计算的引导向量，从训练集构建数据库；该引导向量定义为真实延续与模型自身预测所诱导的表示之间的差值。在测试时，SteerCast为查询历史检索最近邻，聚合其引导向量，并在自回归生成的每一步将所得信号注入预测器的隐藏状态，引导预测轨迹与相似训练案例一致。在不同多变量基准和多个预测 horizon 上的实验表明，SteerCast在微调后的骨干模型和基于检索的基线之上持续提高了预测准确性，且除原始微调外无需额外训练，仅使用训练集作为检索语料库。
英文摘要
Time series forecasting aims to predict future values from historical observations and auxiliary features. We propose \textbf{SteerCast}, a retrieval-based latent steering method that improves decoder-only forecaster at inference time, without updating its parameters. SteerCast constructs a database from the training set by storing a representation of each history window together with a \emph{steering vector} computed in the forecaster's latent space, defined as the difference between representations induced by the ground-truth continuation and by the model's own prediction. At test time, SteerCast retrieves nearest neighbors for a query history, aggregates their steering vectors, and injects the resulting signal into the forecaster's hidden states at every step of autoregressive generation, guiding predictions toward trajectories consistent with similar training cases. Experiments across diverse multivariate benchmarks and multiple horizons show that SteerCast consistently improves forecasting accuracy over the fine-tuned backbone and retrieval-based baselines, while requiring no additional training beyond the original fine-tuning and using only the training set as a retrieval corpus.
