用历史数据检索增强时间序列预测，提升准确率8.4%
Predicting the Future by Retrieving the Past
- 构建全局记忆库，动态检索相似历史模式
- 在7个真实数据集上使主流模型平均性能提升8.4%
- 适合需要高精度与可解释性的时序预测场景
深度学习模型如MLP、Transformer和TCN在单变量时间序列预测中表现卓越，通常依赖历史数据的滑动窗口进行训练。然而，这些模型在推理阶段仅能利用局部上下文，无法显式动态访问全局历史知识，导致丰富的历史模式未被充分使用。为此，我们提出预测未来通过检索过去（PFRP），一种新方法，显式整合全局历史数据以提升预测精度。具体而言，我们构建全局记忆库（GMB）以有效存储和管理全局历史模式，并采用检索机制从GMB中提取相似模式，生成全局预测。通过自适应融合这些全局预测与任意局部预测模型的输出，PFRP产生更准确且可解释的预测。在七个真实世界数据集上的广泛实验表明，PFRP显著提升了先进单变量预测模型的平均性能8.4%。
原文摘要 · Abstract (English)
Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from historical data for training. However, while these models implicitly compress historical information into their parameters during training, they are unable to explicitly and dynamically access this global knowledge during inference, relying only on the local context within the lookback window. This results in an underutilization of rich patterns from the global history. To bridge this gap, we propose Predicting the Future by Retrieving the Past (PFRP), a novel approach that explicitly integrates global historical data to enhance forecasting accuracy. Specifically, we construct a Global Memory Bank (GMB) to effectively store and manage global historical patterns. A retrieval mechanism is then employed to extract similar patterns from the GMB, enabling the generation of global predictions. By adaptively combining these global predictions with the outputs of any local prediction model, PFRP produces more accurate and interpretable forecasts. Extensive experiments conducted on seven real-world datasets demonstrate that PFRP significantly enhances the average performance of advanced univariate forecasting models by 8.4\%. Codes can be found in https://github.com/ddz16/PFRP.
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