Deep Learning For Time Series Data

Version 1.0.2 (1,86 Mo) par MathWorks Student Competitions Team
The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data.
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Mise à jour 23 nov. 2020
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The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data. The first way is using continuous wavelet transform and transfer learning, whereas the second way is using Wavelet Scattering and LSTMs. The explanations of the code are in Chinese. The used data set can be download on：https://github.com/mathworks/physionet_ECG_data/

The video series (in Chinese) on this topic can be found as follows:
https://www.mathworks.com/videos/series/deep-learning-for-time-series-data.html

Citation pour cette source

MathWorks Student Competitions Team (2026). Deep Learning For Time Series Data (https://github.com/mathworks/deep-learning-for-time-series-data/releases/tag/v1.0.2), GitHub. Extrait(e) le .

Compatibilité avec les versions de MATLAB
Créé avec R2020a
Compatible avec les versions R2020a à R2020b
Plateformes compatibles
Windows macOS Linux
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Version Publié le Notes de version
1.0.2

See release notes for this release on GitHub: https://github.com/mathworks/deep-learning-for-time-series-data/releases/tag/v1.0.2
1.0.1

See release notes for this release on GitHub: https://github.com/mathworks/deep-learning-for-time-series-data/releases/tag/v1.0.1
1.0

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Pour consulter ou signaler des problèmes liés à ce module complémentaire GitHub, accédez au dépôt GitHub.