发表机构
Department of Transport and Planning, Faculty of Civil Engineering and Geosciences; Mobility Innovation Centre Delft University of Technology; Delft University of Technology（交通与规划系，土木与地球科学学院; 代尔夫特理工大学移动创新中心; 代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对时间序列预测中预测不足风险高且多数方法忽视峰值预测的问题，提出非对称峰值感知损失APAL，增加对预测不足惩罚及峰值区域训练权重，经评估其能提升尾部准确性和峰值预测质量，平衡总体误差，是实用解决方案。
AI 中文摘要
在许多运营时间序列预测应用中，如人群需求预测，预测不足的风险远高于预测过度。准确预测罕见需求峰值对下游任务至关重要。但多数时间序列预测器采用对称目标训练并主要基于总体误差评估，会掩盖极值和峰值时间预测的失败。我们引入非对称峰值感知损失（APAL），它惩罚预测不足更重且增加每个预测窗口内峰值区域的训练权重。还提出峰值关键评估协议，用通道尾误差和峰值指标补充MAE/MSE。我们在五个先进骨干网络上评估APAL，重点是行人需求预测，结果表明APAL提高了尾部准确性和峰值预测质量，且与总体误差有可控权衡，是峰值预测失败为主要运营问题时的实用解决方案。
英文摘要
In many operational time-series forecasting applications, such as crowd demand forecasting, the risk related to under-prediction is substantially higher than that of over-prediction. Accurate prediction of rare demand spikes plays a critical role in downstream tasks. Yet most time-series forecasters are trained with symmetric objectives (e.g., MSE, MAE) and evaluated primarily on aggregate error, which can mask failures in extreme-values and peak-timing predictions. We introduce Asymmetric Peak-Aware Loss (APAL), a simple, model-agnostic objective that (i) penalizes under-predictions more heavily and (ii) increases the training weight of peak regions within each forecast window. We further propose a peak-critical evaluation protocol that complements MAE/MSE with channel-wise tail error (Top-10% and Top-1%) and peak metrics (precision, recall, F1 under timing tolerance, and peak timing error). We evaluate APAL on long-horizon multivariate forecasting across five state-of-the-art backbones, with a focus on pedestrian demand forecasting using (i) a production-ready subset of the City of Melbourne pedestrian hourly count dataset and (ii) a beach visitor count dataset. The generality of the loss function for time-series forecasting is tested on additional benchmarks. Across peak-critical datasets and settings, APAL improves tail accuracy and peak-prediction quality while exposing a controllable trade-off with aggregate error, making it a practical solution when peak-prediction failures are the dominant operational concern.
