MONSTER: Monash Scalable Time Series Evaluation Repository

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

Abstract: We introduce MONSTER—the MONash Scalable Time Series Evaluation Repository—a collection of large datasets for time series classification and associated set of classification tasks that jointly define a new time series classification benchmark. 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 training set 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.

Certifications: Dataset Certification

Keywords: time series classification, benchmark, dataset, bitter lesson

Video: https://www.youtube.com/watch?v=AiCNj8NC5tk

Code: https://github.com/Navidfoumani/monster

Assigned Action Editor: ~Hugo_Jair_Escalante1

Submission Number: 101