3 citations · 3 across the 2 of their papers we have counts for
3 papers
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…