用隐含均值编码提升多尺度时间序列预测精度
Hierarchical Time Series Forecasting Via Latent Mean Encoding
- 通过分层模块捕捉不同时间粒度的平均趋势
- 在M5数据集上优于TSMixer等主流模型
- 适合需要跨尺度精准预测的商业场景
在多个业务场景中，对目标变量在粗粒度与细粒度时间尺度上的行为进行一致预测，对利润优化决策至关重要，但仍是时间层次预测中的开放问题。本文提出一种新的分层架构，通过专门处理不同时间聚合层级的模块来解决该问题。该架构在隐藏层中学习目标变量的平均行为编码，从而实现对目标时间层次的准确且一致的预测。我们在具有挑战性的真实世界M5数据集上验证了该架构，结果表明其性能优于已有方法，如TSMixer模型。
原文摘要 · Abstract (English)
Coherently forecasting the behaviour of a target variable across both coarse and fine temporal scales is crucial for profit-optimized decision-making in several business applications, and remains an open research problem in temporal hierarchical forecasting. Here, we propose a new hierarchical architecture that tackles this problem by leveraging modules that specialize in forecasting the different temporal aggregation levels of interest. The architecture, which learns to encode the average behaviour of the target variable within its hidden layers, makes accurate and coherent forecasts across the target temporal hierarchies. We validate our architecture on the challenging, real-world M5 dataset and show that it outperforms established methods, such as the TSMixer model.
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