Code for paper titled 'Multivariate Time-series Imputation with Disentangled Temporal Representations'.
Code is written in PyTorch v1.9.0+cu111. Python version is Python 3.6.9.
Simply 'python3 TIDER.py' can conduct the training, validation and testing process.
##Detailed explanations of hyperparameters:
• --save_path: address to save the optimal model parameters.
• --datadir: address of time-series data.
• --device: cpu / gpu device.
• --valid: proportion of validation data.
• --drop_rate: data removing rate
• --eta: hyperparameter for L2 regularization
• --n_test: testing set temporal length
• --num_epochs: number of epochs
• --batch_size: batch size
• --dim_size: dimension of feature matrix
• --lag_list: W of bias matrix
• --lambda_ar: weight for bias matrix constraint function
• --lambda_trend: weight for trend matrix constraint function
• --bias_dimension: dimension for bias feature matrix
• --season_num: K
• --seasonality: seasonality of time-series
• --learning_rate: learning rate
• --lambda_trend: control the loss function of trend matrix
For Guangzhou data: It is a 21461144 tensor, we can first transfer it into a 214*8784 matrix, then use the last 500 columns as the processed matrix
For Westminster data: a csdn blog is close to our processed: https://blog.csdn.net/qq_40206371/article/details/128932640
nearly same as TIDER. The differences lies in:
• no hyperparameter 'seasonality'
• lambda_spatial: control the Laplacian regularization term for matrix U
• topk_freq: the number of frequencies selected in multi-periods seasonality matrix.