分解特征提升交通流预测精度，但对聚合方式敏感。
An Experimental Study on Decomposition-Based Deep Ensemble Learning for Traffic Flow Forecasting
- 将流量数据分解为简单信号后用深度模型集成预测
- 在三个数据集上，分解方法均优于直接建模
- 适合关注时序分解与集成策略的研究者
交通流预测是智能交通系统中的关键任务。深度学习能有效捕捉时间序列数据中的复杂模式，实现高精度预测。然而，深度模型易过度拟合流量数据的细微特征，导致泛化能力差。近期研究提出基于分解的深度集成学习方法，通过将时间序列分解为多个简单信号，再分别训练深度模型并集成以获得最终预测，有望缓解此问题。然而，目前尚缺乏对分解型与非分解型集成方法的系统比较。本文在三个交通数据集上对比了多种分解与非分解型深度集成学习方法。实验结果表明，分解型方法整体表现更优，但其性能对聚合策略和预测时长较为敏感。
原文摘要 · Abstract (English)
Traffic flow forecasting is a crucial task in intelligent transport systems. Deep learning offers an effective solution, capturing complex patterns in time-series traffic flow data to enable the accurate prediction. However, deep learning models are prone to overfitting the intricate details of flow data, leading to poor generalisation. Recent studies suggest that decomposition-based deep ensemble learning methods may address this issue by breaking down a time series into multiple simpler signals, upon which deep learning models are built and ensembled to generate the final prediction. However, few studies have compared the performance of decomposition-based ensemble methods with non-decomposition-based ones which directly utilise raw time-series data. This work compares several decomposition-based and non-decomposition-based deep ensemble learning methods. Experimental results on three traffic datasets demonstrate the superiority of decomposition-based ensemble methods, while also revealing their sensitivity to aggregation strategies and forecasting horizons.
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