Bake off redux: a review and experimental evaluation of recent time series classification algorithms
In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31 (3): 606-
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
In the last 5 years there have been a large number of new time series classification
algorithms proposed in the literature. These algorithms have been evaluated on subsets of …
algorithms proposed in the literature. These algorithms have been evaluated on subsets of …
Convolutional neural networks for time series classification
B Zhao, H Lu, S Chen, J Liu… - Journal of systems …, 2017 - ieeexplore.ieee.org
Time series classification is an important task in time series data mining, and has attracted
great interests and tremendous efforts during last decades. However, it remains a …
great interests and tremendous efforts during last decades. However, it remains a …
MultiRocket: multiple pooling operators and transformations for fast and effective time series classification
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-
of-the-art accuracy with a tiny fraction of the time and without the complex ensembling …
of-the-art accuracy with a tiny fraction of the time and without the complex ensembling …
TS-CHIEF: a scalable and accurate forest algorithm for time series classification
Abstract Time Series Classification (TSC) has seen enormous progress over the last two
decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is …
decades. HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles) is …
Time series classification with ensembles of elastic distance measures
Several alternative distance measures for comparing time series have recently been
proposed and evaluated on time series classification (TSC) problems. These include …
proposed and evaluated on time series classification (TSC) problems. These include …
Adaptively constrained dynamic time warping for time series classification and clustering
Time series classification and clustering are important for data mining research, which is
conducive to recognizing movement patterns, finding customary routes, and detecting …
conducive to recognizing movement patterns, finding customary routes, and detecting …
Proximity forest: an effective and scalable distance-based classifier for time series
Research into the classification of time series has made enormous progress in the last
decade. The UCR time series archive has played a significant role in challenging and …
decade. The UCR time series archive has played a significant role in challenging and …
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 …
Interpretable heartbeat classification using local model-agnostic explanations on ECGs
Abstract Treatment and prevention of cardiovascular diseases often rely on
Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG …
Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG …