Awesome
Codebase for "Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networkss (MRNN)"
Authors: Jinsung Yoon, William R. Zame, Mihaela van der Schaar
Paper: Jinsung Yoon, William R. Zame, Mihaela van der Schaar, "Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks," IEEE Transactions on Biomedical Engineering, 2019.
Paper Link: https://ieeexplore.ieee.org/document/8485748
Contact: jsyoon0823@gmail.com
This directory contains implementations of MRNN framework for imputation in time-series data using GOOGLE stocks dataset.
To run the pipeline for training and evaluation on MRNN framwork, simply run python3 -m main_mrnn.py.
Command inputs:
- file_name: data file name
- seq_len: sequence length of time-series data
- miss_rate: probability of missing components (to be introduced)
- h_dim: hidden state dimensions
- batch_size: the number of samples in mini batch
- iteration: the number of iteration
- learning_rate: learning rate of model training
- metric_name: imputation performance metric
Example command
$ python3 main_mrnn.py --file_name data/google.csv --seq_len 7
--missing_rate: 0.2 --h_dim 10 --batch_size 128 --iteration 2000
--learning_rate 0.01 --metric_name rmse
Outputs
- x: original data with missing
- ori_x: original data without missing
- m: mask matrix
- t: time matrix
- imputed_x: imputed data
- performance: imputation performance