RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
Score
78首次构建如此大规模的真实世界多变量时间序列语料库，并实证其优于合成数据，提供了重要新认知
实验设计严谨，覆盖多种模型和基准，但语料库细节(如具体领域分布)可进一步展开
为时间序列基础模型的训练提供关键数据资源和训练策略，直接推动实际应用落地
当前时间序列基础模型正从单变量向多变量演进，该研究紧跟前沿，但语料库持续更新需求未明确
来自arXiv预印本，作者团队过往工作可靠，且附有开源语料库链接，结果可复现
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper constructs one of the largest real-world multivariate time series corpora and rigorously validates its utility, making a significant contribution to the time series foundation model field.
AI commentary
「This paper is a shot in the arm for the time series foundation model community — it directly addresses the debate over whether synthetic data is sufficient by leveraging massive real-world data. For algorithm engineers and research teams working on time series forecasting, the RMISC corpus itself is a treasure worth prioritizing in their experimental toolkit. More importantly, the results imply that the next wave of TSFM competition may shift from model architecture to data quality.」
Quick overview
Recent years have seen multivariate time series foundation models (TSFMs) predominantly pretrained on synthetic data, which fails to capture real-world complexities. This paper introduces RMISC, a large-scale corpus with about 200 datasets and 142 billion time points across diverse domains. By pretraining four advanced TSFMs on univariate, synthetic multivariate, and real-world multivariate data, experiments show that real-world multivariate data significantly improves zero-shot generalization for both univariate and multivariate TSFMs, providing crucial resources and empirical evidence for stronger models.
- Constructed RMISC, a large-scale real-world multivariate time series corpus with ~200 datasets and 142 billion time points across domains
- Pretrained four advanced TSFMs on univariate, synthetic multivariate, and real-world multivariate data respectively
- Experiments show real-world multivariate data improves zero-shot generalization for both univariate and multivariate TSFMs
- Reveals the critical role of real data in capturing complex temporal dynamics and cross-variable relationships
Full text
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