patrick-kidger/NeuralCDE
Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
This project offers a method for making predictions from complex, real-world data that changes over time, even if measurements are missing or taken at inconsistent intervals. It takes in raw, irregularly sampled time-series data and outputs predictions or classifications. Data scientists and machine learning engineers working with sensor data, financial markets, or medical records would find this useful.
700 stars. No commits in the last 6 months.
Use this if you need to build highly accurate predictive models from time-series data where observations are not uniformly spaced or have gaps.
Not ideal if your time-series data is perfectly regularly sampled and complete, as simpler methods might suffice.
Stars
700
Forks
74
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 22, 2022
Commits (30d)
0
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