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 …
Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles
A recent experimental evaluation assessed 19 time series classification (TSC) algorithms
and found that one was significantly more accurate than all others: the Flat Collective of …
and found that one was significantly more accurate than all others: the Flat Collective of …
Timeclr: A self-supervised contrastive learning framework for univariate time series representation
X Yang, Z Zhang, R Cui - Knowledge-Based Systems, 2022 - Elsevier
Time series are usually rarely or sparsely labeled, which limits the performance of deep
learning models. Self-supervised representation learning can reduce the reliance of deep …
learning models. Self-supervised representation learning can reduce the reliance of deep …
Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification
There have been many new algorithms proposed over the last five years for solving time
series classification (TSC) problems. A recent experimental comparison of the leading TSC …
series classification (TSC) problems. A recent experimental comparison of the leading TSC …
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 …
Time series classification using diversified ensemble deep random vector functional link and resnet features
WX Cheng, PN Suganthan, R Katuwal - Applied Soft Computing, 2021 - Elsevier
Abstract Random Vector Functional Link (RVFL) is popular among researchers in many
areas of machine learning. RVFL is preferred by many researchers as RVFL can produce …
areas of machine learning. RVFL is preferred by many researchers as RVFL can produce …
Multivariate time series classification with parametric derivative dynamic time warping
Multivariate time series (MTS) data are widely used in a very broad range of fields, including
medicine, finance, multimedia and engineering. In this paper a new approach for MTS …
medicine, finance, multimedia and engineering. In this paper a new approach for MTS …
Optimizing dynamic time warping's window width for time series data mining applications
Abstract Dynamic Time Warping (DTW) is a highly competitive distance measure for most
time series data mining problems. Obtaining the best performance from DTW requires …
time series data mining problems. Obtaining the best performance from DTW requires …
Hierarchical clustering of time series data with parametric derivative dynamic time warping
M Łuczak - Expert Systems with Applications, 2016 - Elsevier
Abstract Dynamic Time Warping (DTW) is a popular and efficient distance measure used in
classification and clustering algorithms applied to time series data. By computing the DTW …
classification and clustering algorithms applied to time series data. By computing the DTW …