The tutorials help you brew Partially-Observed Time Series
In this project, partially-observed time series datasets are taken as coffee beans.
As you can see, there is a coffee pot in the PyPOTS logo on the left,
and the tutorials in this repo are going to show you how to use this pot (i.e. PyPOTS) to brew your
coffee beans (i.e. POTS data) into a cup of delicious coffee (i.e. what you want).
For sure, it seems that we also need a coffee grinder to help us grind beans into powder (i.e. preprocessing).
What do you think this grinder will be named? 😉
The tutorials here are for PyPOTS users to quick start their practice, not for achieving the state-of-the-art performance. So we didn't fine tune the hyper-parameters of each models in the tutorials. You can tune the hyper-parameters by yourself to get better performance on the tutorial dataset PhysioNet-2012 or on your own datasets.
Enjoy it! ☕️ And have fun!
❗Note that the tutorials here are for PyPOTS v0.1. Please ensure your installed PyPOTS version is >=0.1.
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