40 citations · 47 across the 3 of their papers we have counts for
4 papers
TSB-SEG: A Systematic Time-Series Segmentation Benchmark
Félix Chavelli, Arik Ermshaus, Fan Yang +3
Time-series segmentation, studied as either Change Point Detection (CPD) or State Detection (SD), underpins a broad range of monitoring and diagnostic applications. However, the fi…
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…
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…
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…