用历史相似数据增强预测，提升时间序列模型表现
Retrieval Augmented Time Series Forecasting
- 从训练集检索与输入最相似的历史模式作为补充信息
- 在10个基准数据集上平均胜率86%，优于主流方法
- 适合需要高精度预测的工业场景应用
时间序列预测通过历史数据中的模式关系来预估未来趋势。本文提出一种检索增强的时间序列预测方法RAFT：在预测未来时，直接从训练集中检索与当前输入最相似的历史数据片段，并利用这些片段的后续真实值与输入共同生成预测结果。该方法通过外部检索模块引入先验知识，增强模型的归纳偏置和学习能力。在10个基准数据集上的实验表明，RAFT在所有任务中均表现优异，平均胜率达86%，显著超越现有基线方法。
原文摘要 · Abstract (English)
Time series forecasting uses historical data to predict future trends, leveraging the relationships between past observations and available features. In this paper, we propose RAFT, a retrieval-augmented time series forecasting method to provide sufficient inductive biases and complement the model's learning capacity. When forecasting the subsequent time frames, we directly retrieve historical data candidates from the training dataset with patterns most similar to the input, and utilize the future values of these candidates alongside the inputs to obtain predictions. This simple approach augments the model's capacity by externally providing information about past patterns via retrieval modules. Our empirical evaluations on ten benchmark datasets show that RAFT consistently outperforms contemporary baselines with an average win ratio of 86%.
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