MONSTER：莫纳什可扩展时间序列评估库

MONSTER: Monash Scalable Time Series Evaluation Repository

February 21, 2025

作者: Angus Dempster, Navid Mohammadi Foumani, Chang Wei Tan, Lynn Miller, Amish Mishra, Mahsa Salehi, Charlotte Pelletier, Daniel F. Schmidt, Geoffrey I. Webb

cs.AI

摘要

我们推出MONSTER——莫纳什可扩展时间序列评估库，这是一个专为时间序列分类而设计的大型数据集集合。时间序列分类领域已从UCR和UEA时间序列分类库设定的通用基准中受益匪浅。然而，这些基准中的数据集规模较小，中位数分别仅为217和255个样本。因此，它们倾向于支持那些在多种小型数据集上优化以达到低分类误差的模型，即那些最小化方差、对计算问题（如可扩展性）考虑较少的模型。我们希望通过引入基于更大数据集的基准，来丰富该领域的研究。我们相信，通过应对从大量数据中有效学习的理论与实践挑战，该领域将迎来巨大的新进展潜力。

English

We introduce MONSTER-the MONash Scalable Time Series Evaluation Repository-a collection of large datasets for time series classification. The field of time series classification has benefitted from common benchmarks set by the UCR and UEA time series classification repositories. However, the datasets in these benchmarks are small, with median sizes of 217 and 255 examples, respectively. In consequence they favour a narrow subspace of models that are optimised to achieve low classification error on a wide variety of smaller datasets, that is, models that minimise variance, and give little weight to computational issues such as scalability. Our hope is to diversify the field by introducing benchmarks using larger datasets. We believe that there is enormous potential for new progress in the field by engaging with the theoretical and practical challenges of learning effectively from larger quantities of data.