发表机构
Keio University（庆应义塾大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对边缘时间序列预测，通过无泄漏评估明确了预热预算、优化器学习率选择等因素对在线自适应收益的影响，提出仅验证的调试流程，为边缘场景的时间序列预测自适应提供了关键指导。
AI 中文摘要
在线自适应可帮助边缘时间序列预测应对分布漂移，但其测得的收益对评估选择十分敏感。我们在无泄漏流协议下研究了6个公开多元流，包括建筑传感器和智能电表数据。我们识别出两个额外的比较偏差来源：第一，静态基线的预热预算具有双向影响：预热不足会导致基线欠训练，而预热过度则会降低其漂移前的泛化性能。在6个数据集-骨干网络设置中，当预热范围为1000至20000步时，估计的自适应收益变化幅度为3.0至18.8个百分点（pp）。第二，在共享默认学习率下对比带动量的SGD（SGD+m）与Adam，会混淆优化器质量与学习率敏感性。我们利用未访问测试数据的漂移前保留验证切片，选择预热预算和各优化器的在线学习率。在仅验证的流程下，Adam在360个评估单元中的310个表现优于SGD+m，同时仍有4个Adam单元低于静态基线。我们进一步针对全量、仅头部及基于校准的自适应，刻画了精度与自适应状态内存、A100测得的每更新延迟的关系。在评估的PatchTST前沿设置中，多个参数高效变体在自适应状态内存轴上是非支配的。智能电表分析还显示，报告的收益取决于电表选择规则。这些发现支持仅验证的调试流程，而目标设备的延迟和能耗仍有待测量。代码、数据及所有报告数值：this https URL。
英文摘要
Online adaptation can help edge time-series forecasting under distribution drift, but its measured benefit is sensitive to evaluation choices. We study six public multivariate streams, including building-sensor and smart-meter data, under a leakage-free streaming protocol. We identify two additional sources of comparison bias. First, the warmup budget of the static baseline has a two-sided effect: insufficient warmup undertrains the baseline, whereas excessive warmup can degrade its pre-drift generalization. Across six dataset-backbone settings, the estimated adaptation benefit changes by 3.0 to 18.8 percentage points (pp) over the 1,000-20,000-step warmup range. Second, comparing SGD with momentum (SGD+m) and Adam at a shared default learning rate conflates optimizer quality with rate sensitivity. We select both the warmup budget and each optimizer's online rate using a held-out pre-drift validation slice without accessing test data. Under this validation-only procedure, Adam outperforms SGD+m in 310 of 360 evaluated cells, while 4 Adam cells remain below the static baseline. We further characterize accuracy against adaptation-state memory and A100-measured per-update latency for full, head-only, and calibration-based adaptation. In the evaluated PatchTST frontier settings, several parameter-efficient variants are nondominated on the adaptation-state-memory axis. Smart-meter analyses also show that reported gains depend on meter-selection rules. These findings support a validation-only commissioning procedure, while target-device latency and energy remain to be measured. Code, data, and all reported numbers: https://github.com/keiotakmin/tsf-edge-adaptation.
Commentsunder review, IEEE BigData 2026
