Dear researchers,
As you know groundwater level time series often deal with missing values.
I have groundwater level time series for 10 years trying to find an appropriate method to impute missed values. I've already read different researches mostly with R packages suggestion for imputing values, but I work with python so I would be grateful if you could suggest any method or algorithm which is better for GWL data.
See the attached file for helpful information. Best wishes, David Booth
This link maybe useful
https://towardsdatascience.com/a-brief-guide-to-data-imputation-with-python-and-r-5dc551a95027
Dear @Valeh you can go through this tuitorial to learn imputing missing data in time series using Python.
https://youtu.be/GEytNZVjZNU
David Eugene Booth I deeply appreciate your helpful suggestion,
Best wishes, Valeh
Dear Proloy Barua , Thank you very much for sharing the link,
Best wishes, Valeh
Dear Somnath Paulchoudhury Thank you for your comment. You are right, actually, the time series is the groundwater level fluctuation with some gaps which need to be predicted so that I can not drop them or simply adding zero instead, as I need the whole fixed data for the next step of my research. I was thinking of using the KNN method for my case. thanks again for your helpful answer. Best wishes, Valeh
Hi.I suggest the following website that could help you:
https://machinelearningmastery.com/handle-missing-data-python/
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