MONSTER：莫納什可擴展時間序列評估資源庫

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

我們推出MONSTER——莫納什可擴展時間序列評估庫，這是一個專為時間序列分類而設的大型數據集集合。時間序列分類領域因UCR和UEA時間序列分類庫設立的通用基準而受益匪淺。然而，這些基準中的數據集規模較小，中位數分別為217和255個樣本。因此，它們傾向於支持那些在各種小型數據集上實現低分類錯誤率優化的模型，即那些最小化方差、對計算問題（如可擴展性）重視不足的模型。我們希望通過引入使用更大數據集的基準來使該領域多樣化。我們相信，通過應對從更大數據量中有效學習的理論與實踐挑戰，該領域具有巨大的新進展潛力。

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.