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Martin Walter

dblp:73/5286 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 70% Spatial and temporal data management · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › time series analysis
time series classification
0.812024
aeon: a Python Toolkit for Learning from Time Series · J. Mach. Learn. Res. 2024
Spatial and temporal data management
time series data
0.812024
aeon: a Python Toolkit for Learning from Time Series · J. Mach. Learn. Res. 2024
Data mining › time series analysis
time series forecasting
0.812024
aeon: a Python Toolkit for Learning from Time Series · J. Mach. Learn. Res. 2024
Data mining › clustering
time series clustering
0.212024
aeon: a Python Toolkit for Learning from Time Series · J. Mach. Learn. Res. 2024

Methods — techniques the papers use, named apart from their topics

scikit-learn API · 0.8
YearPublicationVenuePosition
2024 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 regression and clustering, as well as a variety of utilities, transformations and distance measures designed for time series data. aeon also has a number of experimental modules for tasks such as anomaly detection, similarity search and segmentation. aeon follows the scikit-learn API as much as possible to help new users and enable easy integration of aeon estimators with useful tools such as model selection and pipelines. It provides a broad library of time series algorithms, including efficient implementations of the very latest advances in research. Using a system of optional dependencies, aeon integrates a wide variety of packages into a single interface while keeping the core framework with minimal dependencies. The package is distributed under the 3-Clause BSD license and is available at https://github.com/aeon-toolkit/aeon.
Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer 0001, Anthony J. Bagnall
J. Mach. Learn. Res.9