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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.

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 changed the model input parameter ts_dimension to 300, and run model.fit but it got an error ValueError: Dimensions must be equal, but are 294 and 300 for 'loss/dense_13_loss/sub' (op: 'Sub') with input shapes: [?,294,300], [?,300,300].

I checked your dataset shape and it was (19791, 1050, 1), so when I changed the data shape to (1000,1050,300) it can run well. I guess there's something wrong with the convolution layer, maybe the latent_sample_rate? But I haven't found a solution so far. Could you help me, thank you very much!

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/

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 is where to add it:

About whether "Feature map reduction" should be added before or after Residual Connections in TCN, like:

out = self.net(x)  # net is TCN

res = x if self.downsample is None else self.downsample(x)

return self.relu(out + res), out

Should I go for reduction ‘out’ or ‘self.relu(out + res)’？
Looking forward to your response!

[1]Thill M, Konen W, Wang H, et al. Temporal convolutional autoencoder for unsupervised anomaly detection in time series[J]. Applied Soft Computing, 2021, 112: 107751.