通过事后修正提升时间序列预测的个体准确性
Improving Time Series Forecasting via Instance-aware Post-hoc Revision
- 事后识别偏差实例并用上下文信息修正预测
- 在多个真实数据集上显著降低个体误差
- 适用于任何模型，提升预测可靠性
时间序列预测在诸多实际应用中至关重要，近年来虽借助先进归纳偏置和训练策略取得显著精度提升，但实例级差异仍是重大挑战。这些差异源于分布漂移、缺失数据和长尾模式，常导致特定实例预测不佳，即便整体性能良好。为此，我们提出一种模型无关框架PIR（Post-forecasting Identification and Revision），通过事后识别偏差实例并利用局部与全局上下文信息（包括协变量和历史时序）进行修正。在主流预测模型与多个真实数据集上的实验表明，PIR能有效缓解实例级误差，显著提升预测可靠性。
原文摘要 · Abstract (English)
Time series forecasting plays a vital role in various real-world applications and has attracted significant attention in recent decades. While recent methods have achieved remarkable accuracy by incorporating advanced inductive biases and training strategies, we observe that instance-level variations remain a significant challenge. These variations--stemming from distribution shifts, missing data, and long-tail patterns--often lead to suboptimal forecasts for specific instances, even when overall performance appears strong. To address this issue, we propose a model-agnostic framework, PIR, designed to enhance forecasting performance through Post-forecasting Identification and Revision. Specifically, PIR first identifies biased forecasting instances by estimating their accuracy. Based on this, the framework revises the forecasts using contextual information, including covariates and historical time series, from both local and global perspectives in a post-processing fashion. Extensive experiments on real-world datasets with mainstream forecasting models demonstrate that PIR effectively mitigates instance-level errors and significantly improves forecasting reliability.
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