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 …
Large models for time series and spatio-temporal data: A survey and outlook
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …
applications. They capture dynamic system measurements and are produced in vast …
aeon: a Python toolkit for learning from time series
Abstract aeon is a unified Python 3 library for all machine learning tasks involving time
series. The package contains modules for time series forecasting, classification, extrinsic …
series. The package contains modules for time series forecasting, classification, extrinsic …
WEASEL 2.0: a random dilated dictionary transform for fast, accurate and memory constrained time series classification
A time series is a sequence of sequentially ordered real values in time. Time series
classification (TSC) is the task of assigning a time series to one of a set of predefined …
classification (TSC) is the task of assigning a time series to one of a set of predefined …
Review of Time Series Classification Techniques and Methods
In order to spot trends in the methodologies and procedures employed, this systematic
literature review will look at works on time series categorization. Six research questions are …
literature review will look at works on time series categorization. Six research questions are …
A hands-on introduction to time series classification and regression
Time series classification and regression are rapidly evolving fields that find areas of
application in all domains of machine learning and data science. This hands on tutorial will …
application in all domains of machine learning and data science. This hands on tutorial will …
Convolutional-and Deep Learning-Based Techniques for Time Series Ordinal Classification
Time-series classification (TSC) covers the supervised learning problem where input data is
provided in the form of series of values observed through repeated measurements over time …
provided in the form of series of values observed through repeated measurements over time …
CNN kernels can be the best shapelets
Shapelets and CNN are two typical approaches to model time series. Shapelets aim at
finding a set of sub-sequences that extract feature-based interpretable shapes, but may …
finding a set of sub-sequences that extract feature-based interpretable shapes, but may …
A deep graph kernel-based time series classification algorithm
M Yu, H Huang, R Hou, X Ma, S Yuan - Pattern Analysis and Applications, 2024 - Springer
Time series data are sequences of values that are obtained by sampling a signal at a fixed
frequency, and time series classification algorithms distinguish time series into different …
frequency, and time series classification algorithms distinguish time series into different …
Visemble: A fast ensemble approach for time series classification with multiple visual representations
VMA Souza, PS Veiga, AGR Ribeiro - Knowledge-Based Systems, 2025 - Elsevier
Time series are prevalent data in finance, smart cities, sensor networks, engineering,
bioinformatics, among other domains. These data differ from regular tabular data as they …
bioinformatics, among other domains. These data differ from regular tabular data as they …