8000 Imputation Work Flow on whole dataset · Issue #670 · WenjieDu/PyPOTS · GitHub
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wasay464 opened this issue Mar 23, 2025 · 3 comments
Open

Imputation Work Flow on whole dataset #670

wasay464 opened this issue Mar 23, 2025 · 3 comments
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good first issue Good for newcomers question Further information is requested

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@wasay464
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Issue description

Kudos to the team for the wonderful repo. I have some questions regarding imputation workflow.
I have a custom univariate dataset with half hourly samples and the missing values are in random chunks throughout the dataset. What should I do to get the the imputed values in this case as these NaNs are distributed throughout the train, val and test sets. Furthermore, in this case, the rate of masking is set to be zero as the NaNs values are already masked. So what should be the workflow in this case.
Another question is if i have to implement my own model (say, some modification of TimesNet), how should I add it in the repo. there should be some tutorial for this case as well.

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@wasay464 wasay464 added the question Further information is requested label Mar 23, 2025
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Hi there 👋,

Thank you so much for your attention to PyPOTS! You can follow me on GitHub to receive the latest news of PyPOTS.
If you find PyPOTS helpful to your work, please star⭐️ this repository to get this issue prioritized.
Your star is your recognition, which can help more people notice PyPOTS and grow PyPOTS community.
It matters and is definitely a kind of contribution to the community.

Best,
Wenjie

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github-actions bot commented Apr 7, 2025

This issue had no activity for 14 days. It will be closed in 1 week unless there is some new activity. Is this issue already resolved?

@github-actions github-actions bot added the stale label Apr 7, 2025
@WenjieDu WenjieDu added good first issue Good for newcomers and removed stale labels Apr 8, 2025
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WenjieDu commented Apr 8, 2025

Hi Sultan, thanks for raising the discussion here.

Regarding the workflow, I believe our data preprocessing pipelines in BenchPOTS here https://github.com/WenjieDu/BenchPOTS/blob/main/benchpots/datasets can help. To add the model into PyPOTS, you should refer to some models that you're familiar with in PyPOTS. Their implementations can help you figure out how the framework operates.

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