287 citations · 647 across the 12 of their papers we have counts for
14 papers
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…
Fast and Exact Similarity Search in less than a Blink of an Eye
Patrick Schäfer, Jakob Brand, Ulf Leser +2
Similarity search is a fundamental operation for analyzing data series (DS), which are ordered sequences of real values. To enhance efficiency, summarization techniques are employe…
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…
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…
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…
WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification
Patrick Schäfer, Ulf Leser
A time series is a sequence of sequentially ordered real values in time. Time series classification (TSC) is the task of assigning a time series to one of a set of predefined class…
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…
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…
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
Felix Mujkanovic, Vanja Doskoč, Martin Schirneck +2
Modern time series classifiers display impressive predictive capabilities, yet their decision-making processes mostly remain black boxes to the user. At the same time, model-agnost…
TEASER: Early and Accurate Time Series Classification
P. Schäfer, U. Leser
Early time series classification (eTSC) is the problem of classifying a time series after as few measurements as possible with the highest possible accuracy. The most critical issu…
Multidimensional Range Queries on Modern Hardware
Stefan Sprenger, Patrick Schäfer, Ulf Leser
Range queries over multidimensional data are an important part of database workloads in many applications. Their execution may be accelerated by using multidimensional index struct…
Multivariate Time Series Classification with WEASEL+MUSE
Patrick Schäfer, Ulf Leser
Multivariate time series (MTS) arise when multiple interconnected sensors record data over time. Dealing with this high-dimensional data is challenging for every classifier for at…
Fast and Accurate Time Series Classification with WEASEL
Patrick Schäfer, Ulf Leser
Time series (TS) occur in many scientific and commercial applications, ranging from earth surveillance to industry automation to the smart grids. An important type of TS analysis i…