A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
This is fixing the mentioned issue with as minimal changes as possible, keeping the minimality of this example. But I've seen that in the notebook the imputaton example with saits has already been extended to newer releases from pypots and tsdb.
If this is helpful, feel free to modify the PR such that it is useful for your quickstart example. :) I'm aware that maybe load_specific_dataset could be changed to use BenchPOTS or so, but as this would make the example less minimal for now I refrained from doing so yet...
Best,
Before submitting
[x] This PR is made to fix a typo or improve the docs (you can dismiss the other checks if this is the case).
[ ] Was this discussed/approved via a GitHub issue? Please add a link to it if that's the case.
[ ] I have commented my code, particularly in hard-to-understand areas.
[ ] I have written necessary tests and already run them locally.
What does this PR do?
Fixing #496
This is fixing the mentioned issue with as minimal changes as possible, keeping the minimality of this example. But I've seen that in the notebook the imputaton example with saits has already been extended to newer releases from pypots and tsdb.
If this is helpful, feel free to modify the PR such that it is useful for your quickstart example. :) I'm aware that maybe
load_specific_datasetcould be changed to use BenchPOTS or so, but as this would make the example less minimal for now I refrained from doing so yet...Best,
Before submitting