# LIME-RNN

**Repository Path**: HaixuHe/lime-rnn

## Basic Information

- **Project Name**: LIME-RNN
- **Description**: LIME-RNN论文代码
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-12-12
- **Last Updated**: 2022-04-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# End-to-End Incomplete Time Series Modeling from Linear Memory of Latent Variables

The code for paper "[End-to-End Incomplete Time Series Modeling from Linear Memory of Latent Variables](https://ieeexplore.ieee.org/document/8685795)" accepted by IEEE Transactions on Cybernetics.



# Descriptions
The demo for dataset 'SCITOS G5' with 50% missing ratio  
The published imputation result: 0.170  
data/raw.txt: raw_data for evaluation  
data/miss_data.txt: missing_data  
tf_version: 1.11.0  
python_version: 2.7.17  

# Usage
Usage: python LIMELSTM.py


## Reference


```
@ARTICLE{Ma2019end, 
author={Q. {Ma} and S. {Li} and L. {Shen} and J. {Wang} and J. {Wei} and Z. {Yu} and G. W. {Cottrell}}, 
journal={IEEE Transactions on Cybernetics}, 
title={End-to-End Incomplete Time-Series Modeling From Linear Memory of Latent Variables}, 
year={2019}, 
pages={1-13}, 
ISSN={2168-2267}
}
```