Deep Learning For Time Series Data

Versione 1.0.2 (1,86 MB) da 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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Aggiornato 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

Cita come

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. Recuperato .

Compatibilità della release di MATLAB
Creato con R2020a
Compatibile con R2020a fino a R2020b
Compatibilità della piattaforma
Windows macOS Linux
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Versione Pubblicato Note della release
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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