Deep Learning For Time Series Data

Version 1.0.2 (1.86 MB) by 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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Updated 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

Cite As

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. Retrieved .

MATLAB Release Compatibility
Created with R2020a
Compatible with R2020a to R2020b
Platform Compatibility
Windows macOS Linux
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Version Published Release Notes
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

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.