-
-
Notifications
You must be signed in to change notification settings - Fork 49
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
window truncate function #2
Comments
Hi Haochen, Here are my answers to your questions:
Hope my answers can help you. |
Wenjie, Thank you for your insights and clarification. Follow-ups to your A3: We can use LSTM for the univariable forecasts as an example. Given x[t-n, t-n+1, ... t], the LSTM can be trained to predict x[t+H]. n is the sequence length (i.e., history value provided) and H is the prediction horizon (i.e., how far do we want to predict). The notion is that the further we want to predict the future (i.e., larger H), the harder it gets. I am dealing with a dataset that is much more challenging than the Beijing air quality dataset. The Beijing air quality dataset has simple periodic patterns and most features are correlated. My dataset is more chaotic and also has monotone missing data patterns (i.e., data are continuously missing for a period of time). I guess if I want to apply SAITS to my case, I may need a much a large sequence length so that the continuously missing period only constitutes a small fraction of my sequence. I will follow your suggestion and experiment with different hyperparameters for my case. Thank you again for developing such a great tool, and I am very looking forward to the impact of PyPOTS! |
The follow-up to Regarding your project, I think you can change the artificially missing pattern for SAITS training. In the original work of SAITS, MIT (masked imputation task) randomly masks out some portion of observed values and holds them out to calculate the MIT loss for model optimization. While in your case, now that your data has the monotone missing pattern, you can randomly mask out a continuous period of data in MIT to train SAITS. Please let me know if you want any help with your project from me. Also, thank you very much for your attention to my work. If you have any feedback, please tell me. And you are welcome to join PyPOTS and TSDB community! |
Wenjie,
I have some questions if you do not mind to clarify
Thanks in advance,
Haochen
The text was updated successfully, but these errors were encountered: