Deep Learning For Time Series Data
The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data.
https://github.com/mathworks/deep-learning-for-time-series-data
Sie verfolgen jetzt diese Einreichung
- Aktualisierungen können Sie in Ihrem Feed verfolgter Inhalte sehen.
- Je nach Ihren Kommunikationseinstellungen können Sie auch E-Mails erhalten.
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
Zitieren als
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. Abgerufen .
Allgemeine Informationen
- Version 1.0.2 (1,86 MB)
-
Lizenz auf GitHub anzeigen
Kompatibilität der MATLAB-Version
- Kompatibel mit R2020a bis R2020b
Plattform-Kompatibilität
- Windows
- macOS
- Linux
| Version | Veröffentlicht | Versionshinweise | Action |
|---|---|---|---|
| 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 |
