I’m trying to create a model for anomaly detection like those described in the link below:
which makes use of ECG5000 dataset.
In the above-mentioned example, they make use of a dense layer autoencoder; they train the model with only normal ECG curves; each ECG curvers are made of 140 samples.
In my case, the dataset is made of a time series in which each curve has a different number of samples (they could be 140, 145, 160, 130 and so on).
In your opinion, what could be the best approach to face this problem?
As a possible solution, I thought of padding each curve at the maximum length, but the problem is that I don’t know in advance what could be the maximum duration.
thank for your suggestion,