让预测模型按需聚焦关键时间区间，提升实际应用效果。
Goal-Oriented Time-Series Forecasting: Foundation Framework Design
- 训练时分段加权，推理时动态调整关注区域
- 在目标区间内预测误差降低，下游任务性能提升
- 无需重训即可适配不同应用场景，灵活实用
传统时间序列预测方法通常旨在最小化整体预测误差，未考虑下游应用中不同预测区间的差异重要性。我们提出一种训练方法，使预测模型可在推理时根据应用需求自适应调整关注范围，无需重新训练。该方法在训练阶段将预测空间细分为多个片段，通过动态重加权与聚合，突出应用指定的目标区间。与预先定义关注区间的先前方法不同，本框架支持灵活、按需的调整。在标准基准和新收集的无线通信数据集上的实验表明，该方法不仅提升了目标区间内的预测精度，还显著改善了下游任务表现。结果凸显了预测建模与决策系统深度融合的潜力。
原文摘要 · Abstract (English)
Conventional time-series forecasting methods typically aim to minimize overall prediction error, without accounting for the varying importance of different forecast ranges in downstream applications. We propose a training methodology that enables forecasting models to adapt their focus to application-specific regions of interest at inference time, without retraining. The approach partitions the prediction space into fine-grained segments during training, which are dynamically reweighted and aggregated to emphasize the target range specified by the application. Unlike prior methods that predefine these ranges, our framework supports flexible, on-demand adjustments. Experiments on standard benchmarks and a newly collected wireless communication dataset demonstrate that our method not only improves forecast accuracy within regions of interest but also yields measurable gains in downstream task performance. These results highlight the potential for closer integration between predictive modeling and decision-making in real-world systems.
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