About the script example

[HugoGuillen]

First of all, thank you for all your hard work! This project is very interesting.

I have a comment regarding your script example:

X = StandardScaler().fit_transform(X.to_numpy())
X = X.reshape(num_samples, 48, -1)
X_ori = X  # keep X_ori for validation
X = mcar(X, 0.1)  # randomly hold out 10% observed values as ground truth

Please correct me if I'm wrong, but If you take the hold out AFTER scaling, you already leaked the data distribution to the model training, isn't it?
In that case you, one would need to scale after taking the hold out, and then use these parameters to scale the hold out using those parameters to make a fair evaluation.

Best,
Hugo Guillen.

[WenjieDu]

Hi there,

Thank you so much for your attention to SAITS! If you find SAITS is helpful to your work, please star⭐️ this repository. Your star is your recognition, which can let others notice SAITS. It matters and is definitely a kind of contribution.

I have received your message and will respond ASAP. Thank you again for your patience! 😃

Best,
Wenjie

[WenjieDu]

Hi @HugoGuillen, yes, you're right. It could result in information leakage to the training model.

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