研究人员开发了一个名为Gen-ROTDA的新框架，以改进因不断变化的出行模式而随时间退化的共享单车需求预测模型。该方法利用最优传输来使模型适应新的时间域，用一小部分标记数据拟合目标域锚点并转移残差需求。实验表明，Gen-ROTDA在近期预测任务上实现了最低的平均绝对误差，并且即使在嘈杂、未标记的数据下也表现出稳定性，优于其他最优传输变体。 AI
影响 增强了预测模型在动态环境中的鲁棒性，这对于城市交通等现实世界应用至关重要。
排序理由 该集群包含一篇详细介绍用于特定预测任务的新机器学习框架的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.23115v1 Announce Type: cross Abstract: Bike-sharing models trained on historical station-hour data may degrade when deployed in later years because travel patterns change over time. This paper studies March Citi Bike demand prediction from 2021 to 2026 as a temporal do…
Bike-sharing models trained on historical station-hour data may degrade when deployed in later years because travel patterns change over time. This paper studies March Citi Bike demand prediction from 2021 to 2026 as a temporal domain adaptation problem and proposes Gen-ROTDA, a …