Bake off redux: a review and experimental evaluation of recent time series classification algorithms

M Middlehurst, P Schäfer, A Bagnall - Data Mining and Knowledge …, 2024 - Springer
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 …

The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances

A Bagnall, J Lines, A Bostrom, J Large… - Data mining and …, 2017 - Springer
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 …

Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles

J Lines, S Taylor, A Bagnall - … on Knowledge Discovery from Data (TKDD …, 2018 - dl.acm.org
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 …

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 …

Hive-cote: The hierarchical vote collective of transformation-based ensembles for time series classification

J Lines, S Taylor, A Bagnall - 2016 IEEE 16th international …, 2016 - ieeexplore.ieee.org
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 …

Li-ion battery degradation modes diagnosis via Convolutional Neural Networks

N Costa, L Sánchez, D Anseán, M Dubarry - Journal of Energy Storage, 2022 - Elsevier
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 …

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 …

Multivariate time series classification with parametric derivative dynamic time warping

T Górecki, M Łuczak - Expert Systems with Applications, 2015 - Elsevier
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 …

Optimizing dynamic time warping's window width for time series data mining applications

HA Dau, DF Silva, F Petitjean, G Forestier… - Data mining and …, 2018 - Springer
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 …

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 …