针对异质时间序列，提出两阶段混合模型提升预测精度。
Two-stage hybrid models for enhancing forecasting accuracy on heterogeneous time series
- 先用全局模型识别数据同质性，再分组建模
- 在四个公开数据集上优于六种先进模型
- 适合处理多源异质时间序列的场景
时间序列预测模型通常基于单一序列构建，称为局部模型（tsLM）；而基于多个序列训练的模型称为全局模型（tsGM），可通过学习跨序列信息提升预测精度与泛化能力。然而，当时间序列具有异质性时，tsGM的优势可能难以发挥。尽管增加模型复杂度可帮助适应异质数据，但也可能引发过拟合和预测误差。此外，异质性的定义在文献中尚不明确。为此，本文探讨了数据异质性的定义，并提出一种两阶段建模框架：第一阶段使用tsGM识别同质模式；第二阶段对不同组别分别训练tsLM（如ARIMA）或子全局模型以捕捉异质性。在四个公开数据集上的数值实验表明，该方法显著优于六种现有先进模型，验证了其在异质数据集上释放全局模型潜力的有效性。
原文摘要 · Abstract (English)
A time series forecasting model--which is typically built on a single time series--is known as a local time series model (tsLM). In contrast, a forecasting model trained on multiple time series is referred to as a global time series model (tsGM). tsGMs can enhance forecasting accuracy and improve generalisation by learning cross-series information. As such, developing tsGMs has become a prominent research focus within the time series forecasting community. However, the benefits of tsGMs may not always be realised if the given set of time series is heterogeneous. While increasing model complexity can help tsGMs adapt to such a set of data, it can also increase the risk of overfitting and forecasting error. Additionally, the definition of homogeneity remains ambiguous in the literature. To address these challenges, this paper explores how to define data heterogeneity and proposes a two-stage modelling framework: At stage one, a tsGM is learnt to identify homogeneous patterns; and at stage two, tsLMs (e.g., ARIMA) or sub-tsGMs tailored to different groups are learnt to capture the heterogeneity. Numerical experiments on four open datasets demonstrate that the proposed approach significantly outperforms six state-of-the-art models. These results highlight its effectiveness in unlocking the full potential of global forecasting models for heterogeneous datasets.
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