6 papers · 1 filter
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…
Discovering Leitmotifs in Multidimensional Time Series
Patrick Schäfer, Ulf Leser
A leitmotif is a recurring theme in literature, movies or music that carries symbolic significance for the piece it is contained in. When this piece can be represented as a multi-d…
aeon: a Python toolkit for learning from time series
Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume +8
aeon is a unified Python 3 library for all machine learning tasks involving time series. The package contains modules for time series forecasting, classification, extrinsic regress…
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
Matthew Middlehurst, Patrick Schäfer, Anthony Bagnall
In 2017, a research paper compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the University of California, Riverside (UCR) archive. This study, commonly re…
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…
Motiflets -- Simple and Accurate Detection of Motifs in Time Series
Patrick Schäfer, Ulf Leser
A time series motif intuitively is a short time series that repeats itself approximately the same within a larger time series. Such motifs often represent concealed structures, suc…