title: MONSTER: Monash Scalable Time Series Evaluation Repository
summary: 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.
paper_authors._id: 67bbd6d5ba0bb31293e11210
paper_authors.name: Angus Dempster
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paper_authors.fullname: Angus
paper_authors.type: user
paper_authors.user: angus924
paper_authors._id: 67bbd6d5ba0bb31293e11211
paper_authors.name: Navid Mohammadi Foumani
paper_authors.status: admin_assigned
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paper_authors.fullname: Navid Foumani
paper_authors.type: user
paper_authors.user: Navidfoumani
paper_authors._id: 67bbd6d5ba0bb31293e11212
paper_authors.name: Chang Wei Tan
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/5b3522ceff6b6e8de733898d6b235cc1.svg
paper_authors.fullname: Chang Wei Tan
paper_authors.type: user
paper_authors.user: charsiuu
paper_authors._id: 67bbd6d5ba0bb31293e11213
paper_authors.name: Lynn Miller
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paper_authors.fullname: Lynn Miller
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paper_authors.user: lynn-miller
paper_authors._id: 67bbd6d5ba0bb31293e11214
paper_authors.name: Amish Mishra
paper_authors._id: 67bbd6d5ba0bb31293e11215
paper_authors.name: Mahsa Salehi
paper_authors._id: 67bbd6d5ba0bb31293e11216
paper_authors.name: Charlotte Pelletier
paper_authors._id: 67bbd6d5ba0bb31293e11217
paper_authors.name: Daniel F. Schmidt
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:39:39.384Z
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paper_authors.fullname: Daniel Schmidt
paper_authors.type: user
paper_authors.user: DanielSchmidt
paper_authors._id: 67bbd6d5ba0bb31293e11218
paper_authors.name: Geoffrey I. Webb
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:39:48.897Z
paper_authors._id: 64a7301b01646254506b2746
paper_authors.avatarUrl: /avatars/910efc38bf28f4250014483095c7b552.svg
paper_authors.fullname: Geoffrey Webb
paper_authors.type: user
paper_authors.user: geoffwebb
paper_title: MONSTER: Monash Scalable Time Series Evaluation Repository
paper_summary: 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.
paper_ai_summary: A new repository, MONSTER, aimed at diversifying time series classification by providing larger datasets to challenge traditional models and improve scalability.
paper_ai_keywords: time series classification MONSTER UCR UEA scalability