The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Abstract Time Series Classification (TSC) involves building predictive models for a discrete
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …
target variable from ordered, real valued, attributes. Over recent years, a new set of TSC …
HIVE-COTE 2.0: a new meta ensemble for time series classification
Abstract The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE)
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …
is a heterogeneous meta ensemble for time series classification. HIVE-COTE forms its …
UniTS: A unified multi-task time series model
Although pre-trained transformers and reprogrammed text-based LLMs have shown strong
performance on time series tasks, the best-performing architectures vary widely across …
performance on time series tasks, the best-performing architectures vary widely across …
The UEA multivariate time series classification archive, 2018
In 2002, the UCR time series classification archive was first released with sixteen datasets. It
gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. In …
gradually expanded, until 2015 when it increased in size from 45 datasets to 85 datasets. In …
RTFN: A robust temporal feature network for time series classification
Time series data usually contains local and global patterns. Most of the existing feature
networks focus on local features rather than the relationships among them. The latter is also …
networks focus on local features rather than the relationships among them. The latter is also …
The canonical interval forest (CIF) classifier for time series classification
Time series classification (TSC) is home to a number of algorithm groups that utilise different
kinds of discriminatory patterns. One of these groups describes classifiers that predict using …
kinds of discriminatory patterns. One of these groups describes classifiers that predict using …
Time-series classification in smart manufacturing systems: An experimental evaluation of state-of-the-art machine learning algorithms
Manufacturing is transformed towards smart manufacturing, entering a new data-driven era
fueled by digital technologies. The resulting Smart Manufacturing Systems (SMS) gather …
fueled by digital technologies. The resulting Smart Manufacturing Systems (SMS) gather …
Z-Time: efficient and effective interpretable multivariate time series classification
Multivariate time series classification has become popular due to its prevalence in many real-
world applications. However, most state-of-the-art focuses on improving classification …
world applications. However, most state-of-the-art focuses on improving classification …
Xcm: An explainable convolutional neural network for multivariate time series classification
Multivariate Time Series (MTS) classification has gained importance over the past decade
with the increase in the number of temporal datasets in multiple domains. The current state …
with the increase in the number of temporal datasets in multiple domains. The current state …
Li-ion battery degradation modes diagnosis via Convolutional Neural Networks
Lithium-ion batteries are ubiquitous in modern society with a presence in storage systems,
electric cars, portable electronics, and many more applications. Consequently, to enable …
electric cars, portable electronics, and many more applications. Consequently, to enable …