40 citations · 47 across the 3 of their papers we have counts for
3 papers
cs.LG2025
CLaP -- State Detection from Time Series
Arik Ermshaus, Patrick Schäfer, Ulf Leser
The ever-growing amount of sensor data from machines, smart devices, and the environment leads to an abundance of high-resolution, unannotated time series (TS). These recordings en…
cs.LG2023★ 7 cited
Raising the ClaSS of Streaming Time Series Segmentation
Arik Ermshaus, Patrick Schäfer, Ulf Leser
Ubiquitous sensors today emit high frequency streams of numerical measurements that reflect properties of human, animal, industrial, commercial, and natural processes. Shifts in su…
cs.LG2022★ 40 cited
ClaSP -- Parameter-free Time Series Segmentation
Arik Ermshaus, Patrick Schäfer, Ulf Leser
The study of natural and human-made processes often results in long sequences of temporally-ordered values, aka time series (TS). Such processes often consist of multiple states, e…