isabella232 / vsf-time-series

Code for the paper - Multi-Variate Time Series Forecasting on Variable Subsets accepted at KDD 2022 Research Track.

Running the model

Datasets - METR-LA, SOLAR, TRAFFIC, ECG. This code provides a running example with all components on MTGNN model (we acknowledge the authors of the work).

Standard Training

python train_multi_step.py --data ./data/{0} --model_name {1} --device cuda:0 --expid {2} --epochs 100 --batch_size 64 --runs 10 --random_node_idx_split_runs 100 --lower_limit_random_node_selections 15 --upper_limit_random_node_selections 15 --step_size1 {3} --mask_remaining {4}

Here,
{0} - refers to the dataset directory: ./data/{ECG/TRAFFIC/METR-LA/SOLAR}
{1} - refers to the model name
{2} - refers to the manually assigned "ID" of the experiment
{3} - step_size1 is 2500 for METR-LA and SOLAR, 400 for ECG, 1000 for TRAFFIC
{4} - inference post training in the partial setting, set to true or false. Note - mask_remaining is the alias for "Partial" setting in the paper

• random_node_idx_split_runs - the number of randomly sampled subsets per trained model run
• lower_limit_random_node_selections and upper_limit_random_node_selections - the percentage of variables in the subset S.

Training with predefined subset S, the S apriori setting

python train_multi_step.py --data ./data/{0} --model_name {1} --device cuda:0 --expid {2} --epochs 100 --batch_size 64 --runs 50 --predefined_S --random_node_idx_split_runs 1 --lower_limit_random_node_selections 100 --upper_limit_random_node_selections 100 --step_size1 {3}

Training the model with Identity matrix as Adjacency

python train_multi_step.py --data ./data/{0} --model_name {1} --device cuda:0 --expid {2} --epochs 100 --batch_size 64 --runs 10 --adj_identity_train_test --random_node_idx_split_runs 100 --lower_limit_random_node_selections 100 --upper_limit_random_node_selections 100 --step_size1 {3}

Inference

Partial setting inference

python train_multi_step.py --data ./data/{0} --model_name {1} --device cuda:0 --expid {2} --epochs 0 --batch_size 64 --runs 10 --random_node_idx_split_runs 100 --lower_limit_random_node_selections 15 --upper_limit_random_node_selections 15 --mask_remaining True

• Note that epochs are set to 0 and mask_remaining (alias of "Partial" setting in the paper) to True

Oracle setting inference

python train_multi_step.py --data ./data/{0} --model_name {1} --device cuda:0 --expid {2} --epochs 0 --batch_size 64 --runs 10 --random_node_idx_split_runs 100 --lower_limit_random_node_selections 100 --upper_limit_random_node_selections 100 --do_full_set_oracle true --full_set_oracle_lower_limit 15 --full_set_oracle_upper_limit 15

Our Wrapper Technique

python train_multi_step.py --data ./data/{0} --model_name {1} --device cuda:0 --expid {2} --epochs 0 --batch_size 64 --runs 10 --random_node_idx_split_runs 100 --lower_limit_random_node_selections 15 --upper_limit_random_node_selections 15 --borrow_from_train_data true --num_neighbors_borrow 5 --dist_exp_value 0.5 --neighbor_temp 0.1 --use_ewp True

Requirements

The model is implemented using Python3 with dependencies specified in requirements.txt

Data Preparation

Multivariate time series datasets

Download Solar and Traffic datasets from https://github.com/laiguokun/multivariate-time-series-data. Uncompress them and move them to the data folder.

Download the METR-LA dataset from Google Drive or Baidu Yun provided by Li et al.. Move them into the data folder. (Optinally - download the adjacency matrix for META-LA from here and put it as ./data/sensor_graph/adj_mx.pkl , as shown below):

wget https://github.com/nnzhan/MTGNN/blob/master/data/sensor_graph/adj_mx.pkl
mkdir data/sensor_graph
mv adj_mx.pkl data/sensor_graph/

Download the ECG5000 dataset from time series classification.

# Create data directories
mkdir -p data/{METR-LA,SOLAR,TRAFFIC,ECG}

# for any dataset, run the following command
python generate_training_data.py --ds_name {0} --output_dir data/{1} --dataset_filename data/{2}

Here
{0} is for the dataset: metr-la, solar, traffic, ECG
{1} is the directory where to save the train, valid, test splits. These are created from the first command
{2} the raw data filename (the downloaded file), such as - ECG_data.csv, metr-la.hd5, solar.txt, traffic.txt

Citation