3 citations · 3 across the 4 of their papers we have counts for
5 papers · 1 filter
Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification
Raphael Fischer, Angus Dempster, Sebastian Buschjäger +3
Time series classification (TSC) enables important use cases, however lacks a unified understanding of performance trade-offs across models, datasets, and hardware. While resource…
The Multiverse of Time Series Machine Learning: an Archive for Multivariate Time Series Classification
Matthew Middlehurst, Aiden Rushbrooke, Ali Ismail-Fawaz +6
Time series machine learning (TSML) is a growing research field that spans a wide range of tasks. The popularity of established tasks such as classification, clustering, and extrin…
MONSTER: Monash Scalable Time Series Evaluation Repository
Angus Dempster, Navid Mohammadi Foumani, Chang Wei Tan +6
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 ha…
HYDRA: Competing convolutional kernels for fast and accurate time series classification
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods ba…
MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification
Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb
Until recently, the most accurate methods for time series classification were limited by high computational complexity. ROCKET achieves state-of-the-art accuracy with a fraction of…