MONSTER: Monash Scalable Time Series Evaluation Repository
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.
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Angus Dempster
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2025-02-24T02:18:57.914Z
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Angus
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angus924
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Navid Mohammadi Foumani
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2025-02-25T16:39:04.191Z
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Navid Foumani
user
Navidfoumani
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Chang Wei Tan
admin_assigned
2025-02-25T16:39:12.684Z
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Chang Wei Tan
user
charsiuu
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Lynn Miller
admin_assigned
2025-02-25T16:39:20.335Z
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Lynn Miller
user
lynn-miller
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Amish Mishra
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Mahsa Salehi
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Charlotte Pelletier
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Daniel F. Schmidt
admin_assigned
2025-02-25T16:39:39.384Z
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Daniel Schmidt
user
DanielSchmidt
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Geoffrey I. Webb
admin_assigned
2025-02-25T16:39:48.897Z
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Geoffrey Webb
user
geoffwebb
MONSTER: Monash Scalable Time Series Evaluation Repository
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.
A new repository, MONSTER, aimed at diversifying time series classification by providing larger datasets to challenge traditional models and improve scalability.
time series classification MONSTER UCR UEA scalability