bioma-tcn-ae
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MarkusThill
Minimal Working Example of a (baseline) Temporal Convolutional Autoencoder (TCN-AE) for Anomaly Detection in Time Series
`Conv1DTranspose` layer of Keras shall be used instead of `UpSampling1D` since it only does upsampling, not deconvolution. Place in the code: https://github.com/MarkusThill/bioma-tcn-ae/blob/main/src/tcnae.py#L127 Conv1DTranspose: https://keras.io/api/layers/convolution_layers/convolution1d_transpose/
Hi, I want to test TCN-AE on my own dataset, and there is a problem, could you help me see how to solve it? My dataset shape is **(1000,300,300)**, I...
**Using padding=same for time series data will refer to future data. Can you elaborate on this part? Thank you very much for your answer.**
As for the **Feature map reduction** mentioned in the article[1], I did not find it in your code or keras_tcn. So I have a problem with adding this layer, which...
Hi, I am studying your research and have a few questions regarding your code. In Section 2.6.3, "Utilizing hidden representations for the anomaly detection task," you took the blue bar...
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Minimal Working Example of a (baseline) Temporal Convolutional Autoencoder (TCN-AE) for Anomaly Detection in Time Series