TSEst is a multi-modal time series imputation model that can use an additional modality (another cross-sectional or time-series data) to impute missing values in a time series data. This multi-modal approach shows improved performance over uni-modal imputation models.
Environment can be created using the command conda env create -f my_conda_env.yml. my_conda_env.yml is provided in the repository.
Download a sample data (Daymet) from this link into the parent directory. Run python3 run_models.py --config_path configs/Camel_Transformer_best_rnd.ini to train the model. Modify the values of <model_saving_dir> and