不同时间序列用不同模型，自适应融合提升预测效果
Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting
- 根据输入特征动态分配各模型权重，实现样本级自适应融合
- 在长短期预测任务中均超越现有单模型表现，实现普遍提升
- 可跨数据集泛化，适合需要稳定高精度的工业场景
时间序列预测在众多实际应用中至关重要。尽管已有强大模型在基准数据集上取得优异结果，但通过细粒度的样本级分析发现：(i) 没有单一模型在所有测试样本上始终领先；(ii) 各模型仅在特定情况下表现更优。这一现象促使我们探索如何针对不同样本自适应地利用各模型的优势。本文提出 TimeFuse 框架，实现异构模型在样本级的自适应融合。该框架利用元特征刻画输入时间序列，并训练一个可学习的融合器，为任意输入预测最优的模型融合权重。融合器可通过来自多种数据集的样本联合训练，从而适应多样化的时序模式，具备对新数据甚至未见数据集的泛化能力。大量实验表明，TimeFuse 在各类长短时预测任务中均显著优于当前最先进单模型，实现近乎普适的性能提升。代码已开源：https://github.com/ZhiningLiu1998/TimeFuse。
原文摘要 · Abstract (English)
Time-series forecasting plays a critical role in many real-world applications. Although increasingly powerful models have been developed and achieved superior results on benchmark datasets, through a fine-grained sample-level inspection, we find that (i) no single model consistently outperforms others across different test samples, but instead (ii) each model excels in specific cases. These findings prompt us to explore how to adaptively leverage the distinct strengths of various forecasting models for different samples. We introduce TimeFuse, a framework for collective time-series forecasting with sample-level adaptive fusion of heterogeneous models. TimeFuse utilizes meta-features to characterize input time series and trains a learnable fusor to predict optimal model fusion weights for any given input. The fusor can leverage samples from diverse datasets for joint training, allowing it to adapt to a wide variety of temporal patterns and thus generalize to new inputs, even from unseen datasets. Extensive experiments demonstrate the effectiveness of TimeFuse in various long-/short-term forecasting tasks, achieving near-universal improvement over the state-of-the-art individual models. Code is available at https://github.com/ZhiningLiu1998/TimeFuse.
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