Could I use the imputation methods to upsample my low-frequency data?

[mike-egg123]

Issue description

Hello, Mr. Du! I am a graduate student from the School of Software at Beihang University, majoring in time series analysis. I have been researching time series imputation recently and happened to see your related research, including papers and codes, which has greatly inspired me. Now I have a small question to ask you, can the imputation method be applied to the task of upsampling low-frequency data? My initial idea is to use the resample method of data analysis libraries such as Pandas to resample low-frequency data at a higher frequency. At this time, there will be many missing data. For these missing data, use the model in your PYPOTS library for imputation. Do you think this approach is reasonable?

[github-actions]

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. 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.

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

Best,
Wenjie

[WenjieDu]

Hi @mike-egg123, worth a shot, but the final results depend on your data's attributes, e.g. the original sampling freq of raw data. You can reach out to me on WeChat.

[mike-egg123]

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[github-actions]

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