mvts-imputation

Multivariate Time Series Imputation by Graph Neural Networks and other methods

Note that, given the size of the files, the datasets are not readily available in the folder. See the next section for the downloading instructions.

Datasets

All the datasets used in the experiment, except CER-E, are open and can be downloaded from this link. The CER-E dataset can be obtained free of charge for research purposes following the instructions at this link. We recommend storing the downloaded datasets in a folder named datasets inside this directory.

Configuration files

The config directory stores all the configuration files used to run the experiment. They are divided into folders, according to the model.

Library

The support code, including the models and the datasets readers, are packed in a python library named lib. Should you have to change the paths to the datasets location, you have to edit the __init__.py file of the library.

Scripts

The scripts used for the experiment in the paper are in the scripts folder.

run_baselines.py is used to compute the metrics for the MEAN, KNN, MF and MICE imputation methods. An example of usage is
```
 python ./scripts/run_baselines.py --datasets air36 air --imputers mean knn --k 10 --in-sample True --n-runs 5
```
run_imputation.py is used to compute the metrics for the deep imputation methods. An example of usage is
```
 python ./scripts/run_imputation.py --config config/grin/air36.yaml --in-sample False
```
run_synthetic.py is used for the experiments on the synthetic datasets. An example of usage is
```
 python ./scripts/run_synthetic.py --config config/grin/synthetic.yaml --static-adj False
```

Requirements