用相关系统的长数据，帮短数据系统做精准预测。
Tailored Forecasting from Short Time Series via Meta-learning
- 通过元学习从相关系统中迁移知识，初始化目标模型。
- 在仅有短时序数据下，仍能准确预测短期动态与长期统计特征。
- 无需标签，对行为差异大的系统也有效，适合数据稀缺场景。
机器学习模型可有效预测动态系统，但通常需要大量历史数据，导致短时序数据的预测极具挑战。为此，我们提出元学习驱动的定制化预测方法（METAFORS），通过跨系统知识迁移，在数据有限场景下实现精准预测。该方法基于一个由长时序数据训练的模型库，学习通用规律并初始化针对目标系统的模型。采用储层计算实现，并在模拟混沌系统上测试，结果表明，即使测试系统与相关系统行为显著不同，METAFORS仍能可靠预测短期动态和长期统计特性，且无需上下文标签。这凸显了其在数据稀缺场景下的强大泛化能力。
原文摘要 · Abstract (English)
Machine learning models can effectively forecast dynamical systems from time-series data, but they typically require large amounts of past data, making forecasting particularly challenging for systems with limited history. To overcome this, we introduce Meta-learning for Tailored Forecasting using Related Time Series (METAFORS), which generalizes knowledge across systems to enable forecasting in data-limited scenarios. By learning from a library of models trained on longer time series from potentially related systems, METAFORS builds and initializes a model tailored to short time-series data from the system of interest. Using a reservoir computing implementation and testing on simulated chaotic systems, we demonstrate that METAFORS can reliably predict both short-term dynamics and long-term statistics without requiring contextual labels. We see this even when test and related systems exhibit substantially different behaviors, highlighting METAFORS' strengths in data-limited scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。