解决时间序列尺度差异导致的预测偏差问题。
Self-Adaptive Scale Handling for Forecasting Time Series with Scale Heterogeneity
- 自适应学习每个序列的缩放因子，避免统一归一化损失信息。
- 在蚂蚁财富和支付宝真实基金销售数据上提升模型精度。
- 适合处理跨量级时间序列的工业级预测场景。
当前时间序列预测研究多聚焦于尺度同质数据，但实际工业场景如金融产品销售中，不同序列常存在数量级差异（尺度异质性）。由于这些序列具有相似的时间模式，联合建模可提升数据利用效率。然而现有缩放方法或压缩低尺度信号（全局归一化），或破坏语义区分度并放大反向缩放误差（窗口级缩放）。本文提出自适应尺度处理（AS）模块，通过神经网络学习每个输入的自适应缩放因子，在保留语义区分度的同时减少反向缩放误差。AS包含尺度校准（SC）与缩放选择（SS）两部分，前者校准先验均值缩放因子，后者决定是否应用校准以避免过度校准。在蚂蚁财富和支付宝的真实基金销售数据集上实验表明，AS可无缝集成至主流时序预测模型，持续提升性能。代码与数据集已公开于 https://github.com/Meteor-Stars/ASTSF。
原文摘要 · Abstract (English)
Current time series forecasting (TSF) research predominantly focuses on scale-homogeneous data, where different time series share similar numerical magnitude ranges. However, in real-world industrial scenarios such as financial product sales, different time series often differ by orders of magnitude (scale heterogeneity). Since these series share similar temporal patterns, joint modeling is desirable for better data utilization, yet existing scaling methods either compress low-scale signals (global normalization) or destroy semantic discriminability and amplify inverse-scaling errors (window-based scaling). This paper proposes a self-Adaptive Scale-handling (AS) module that learns adaptive scale factors tailored to each input, preserving semantic discriminability while reducing inverse-scaling errors. AS consists of Scale Calibrating (SC), which calibrates prior mean scaling factors through neural networks, and Scaling Selection (SS), which decides whether to apply calibration or retain the original factor, avoiding over-calibration. Experiments on real-world fund sales datasets from Ant Fortune and Alipay show that AS seamlessly integrates into popular TSF models and consistently improves their performance. The code and dataset are available at the link https://github.com/Meteor-Stars/ASTSF.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。