Is this X_tilde_3 or X_c?

[lzylzylllll]

        imputation_MAE = masked_mae_cal(
            X_tilde_3, inputs["X_holdout"], inputs["indicating_mask"]
        )

[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]

Yeah, I know this. No worries. The parts in X_tilde_3 and X_c for this loss calculation are exactly the same.

[lzylzylllll]

Do you mean X_tilde_3 and X_c values are equal?

[WenjieDu]

Of course they are not equal, there is code here to generate X_c from X_tilde_3 https://github.com/WenjieDu/SAITS/blob/main/modeling/saits.py#L193-L196. I mean the exactly what I said above the parts in X_tilde_3 and X_c for this loss calculation are exactly the same. You can replace X_tilde_3 with X_c in that line of code calculating imputation_MAE. The result will be the same.

[lzylzylllll]

I understand, thanks for the answer

[WenjieDu]

Absolutely my pleasure. If it doesn't bother you, please star 🌟 this repo to help more people notice this useful work. Thanks.

[WenjieDu]

I'm closing this issue due to all questions solved. BTW, if you're interested in time series modeling, PyPOTS may be useful to you. Please pay a visit to https://pypots.com/ to know more about it. 😊