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Found 11 papers, 11 papers with code
Raising the ClaSS of Streaming Time Series Segmentation
Ubiquitous sensors today emit high frequency streams of numerical measurements that reflect properties of human, animal, industrial, commercial, and natural processes.
Human Activity Segmentation Challenge @ ECML/PKDD’23
Despite its importance, existing methods demonstrate limited efficacy on real-world multivariate time series data.
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
We introduce 30 classification datasets either recently donated to the archive or reformatted to the TSC format, and use these to further evaluate the best performing algorithm from each category.
Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark
We provide, for the first time, a systematic survey and experimental study of 6 TS window size selection (WSS) algorithms on three diverse TSDM tasks, namely anomaly detection, segmentation and motif discovery, using state-of-the art TSDM algorithms and benchmarks.
WEASEL 2.0 -- A Random Dilated Dictionary Transform for Fast, Accurate and Memory Constrained Time Series Classification
Time series classification (TSC) is the task of assigning a time series to one of a set of predefined classes, usually based on a model learned from examples.
ClaSP -- Parameter-free Time Series Segmentation
Such processes often consist of multiple states, e. g. operating modes of a machine, such that state changes in the observed processes result in changes in the distribution of shape of the measured values.
Ranked #1 on
Change Point Detection
on TSSB
(Covering metric)
Motiflets -- Simple and Accurate Detection of Motifs in Time Series
Motif discovery (MD) is the task of finding such motifs in a given input series.
ClaSP - Time Series Segmentation
In our experimental evaluation using a benchmark of 98 datasets, we show that ClaSP outperforms the state-of-the-art in terms of accuracy and is also faster than the second best method.
Ranked #1 on
Change Point Detection
on TSSB
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
Modern time series classifiers display impressive predictive capabilities, yet their decision-making processes mostly remain black boxes to the user.
Multivariate Time Series Classification with WEASEL+MUSE
Multivariate time series (MTS) arise when multiple interconnected sensors record data over time.
Fast and Accurate Time Series Classification with WEASEL
On the popular UCR benchmark of 85 TS datasets, WEASEL is more accurate than the best current non-ensemble algorithms at orders-of-magnitude lower classification and training times, and it is almost as accurate as ensemble classifiers, whose computational complexity makes them inapplicable even for mid-size datasets.
