---
title: "RMISC：大规模真实世界多变量时间序列基础模型语料库"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.06504v1"
permalink: "https://feed.astralor.com/items/itm_02c24e08ea1820dcc9636ee7"
published: "2026-07-07T17:04:44.000Z"
score: 78
channel: "ai"
tags: ["RMISC","多变量时间序列","时间序列基础模型","真实世界数据","零样本泛化"]
---

# RMISC：大规模真实世界多变量时间序列基础模型语料库

## AI 点评

> 这篇论文对时间序列基础模型社区来说是一剂强心针——它用大规模真实数据直接回应了“合成数据是否足够好”的争议。对于从事时序预测的算法工程师和研究团队，RMISC语料库本身就是一个宝藏，值得优先纳入实验工具箱。更重要的是，实验结果暗示：下一代TSFMs的竞争可能从模型架构转向数据质量。

## 速览

近年来，多变量时间序列基础模型（TSFMs）主要依赖合成数据进行预训练，难以捕捉真实世界的复杂动态。本文构建了RMISC语料库，包含约200个数据集、1420亿时间点，覆盖多个领域。在单变量、合成多变量和真实世界多变量数据上预训练四种先进TSFMs并评估零样本泛化能力，实验表明真实世界多变量数据显著提升模型性能，为更强大的时间序列基础模型提供了关键资源与实证依据。

## 关键点

- 构建了大规模真实世界多变量时间序列语料库RMISC，含约200个数据集、1420亿时间点，覆盖多领域
- 分别在单变量、合成多变量和真实世界多变量数据上预训练四种先进TSFMs
- 实验表明真实世界多变量数据能提升单变量和多变量TSFMs的零样本泛化能力
- 揭示了真实数据对捕捉复杂时序动态和跨变量关系的关键作用

## 全文

Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization. Modern multivariate TSFMs are predominantly pretrained on multivariate synthetic data, which is easier to scale but may fail to capture the complex temporal dynamics and cross-variable relationships present in real-world time series. This raises a key question: Whether and to what extent the leading TSFMs trained with the real-world corpus perform better than those trained with synthetic data? To answer this, we establish the RMISC corpus, a considerably large-scale, high-quality, openly accessible, real-world, and multivariate time series archive that contains around 200 datasets and 142 billion time points across diverse domains. Furthermore, we pretrain four advanced TSFMs on univariate, synthetic multivariate, and real-world multivariate data and evaluate their zero-shot generalization capabilities on standard in-distribution and out-of-distribution benchmarks. Experimental results show that incorporating real-world multivariate data predominantly improves the generalization performance for both univariate and multivariate TSFMs. These results provide a deeper understanding of how real-world multivariate data contributes to the development of stronger TSFMs.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_02c24e08ea1820dcc9636ee7