Description: This package provides an implementation of the `Learning Individual Features (LIFE)`
[1] approach for MTS with missing values.
References:
[1] Zhao-Yu Zhang, Shao-Qun Zhang, Yuan Jiang, and Zhi-Hua Zhou. LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values. In: Proceedings of the 21st IEEE International Conference on Data Mining (ICDM'21), Auckland, New Zealand, 2021.
ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact
Prof. Zhi-Hua Zhou.
ATTN2: This package was developed by
Mr. Zhao-Yu Zhang. For any problem concerning the code, please feel free to contact Mr. Zhao-Yu Zhang.
Requirement: The code was developed with the following Python3 packages.
* dtaidistance>=1.2.3
* numpy>=1.17.3
* POT>=2.2.3
* PyYAML>=5.1.2
* scikit-learn>=0.21.3
* seaborn>=0.10.1
* sktime>=0.6.0
* torch>=1.2.0
* torchvision>=0.4.1
* ujson>=3.0.0
Download: [
code] (7.45MB)