This is the code produced as part of the paper Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts
Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts.
Prathamesh Deshpande and Sunita Sarawagi. IJCAI 2022. arXiv:2111.03394v1.
- Python 3.7.9 (recommended).
- All the requirements are specified in requirements.txt.
./script.sh
- If an optional argument is not passed, it's value will be extracted from configuration specified in the file
main.py
(based ondataset_name
,model_name
). - If a valid argument value is passed through command line arguments, the code will use it further. That is, it will ignore the value assigned in the configuration.
Argument name | Type | Valid Assignments | Default |
---|---|---|---|
dataset_name | str | azure, ett, etthourly, Solar, taxi30min, Traffic911 | positional argument |
saved_models_dir | str | - | None |
output_dir | str | - | None |
N_input | int | >0 | -1 |
N_output | int | >0 | -1 |
epochs | int | >0 | -1 |
normalize | str | same, zscore_per_series, gaussian_copula, log | None |
learning_rate | float | >0 | -1.0 |
hidden_size | int | >0 | -1 |
num_grulstm_layers | int | >0 | -1 |
batch_size | int | >0 | -1 |
v_dim | int | >0 | -1 |
t2v_type | str | local, idx, mdh_lincomb, mdh_parti | None |
K_list | [int,...,int ] | [>0,...,>0 ] | [] |
device | str | - | None |
All the datasets can be found here.
Add the dataset files/directories in data
directory before running the code.
Following output files are stored in the <output_dir>/<dataset_name>/
directory.
File name | Description |
---|---|
inputs.npy | Test input values, size: number of time-series x N_input |
targets.npy | Test target/ground-truth values, size: number of time-series x N_output |
<model_name> _pred_mu.npy |
Mean forecast values. The size of the matrix is number of time-series x number of time-steps |
<model_name> _pred_std.npy |
Standard-deviation of forecast values. The size of the matrix is number of time-series x number of time-steps |
All the evaluation metrics on test data are stored in <output_dir>/results_<dataset_name>.json
in the following format:
{
<model_name1>:
{
'crps':<crps>,
'mae':<mae>,
'mse':<mse>,
'smape':<smape>,
'dtw':<dtw>,
'tdi':<tdi>,
}
<model_name2>:
{
'crps':<crps>,
'mae':<mae>,
'mse':<mse>,
'smape':<smape>,
'dtw':<dtw>,
'tdi':<tdi>,
}
.
.
.
}
Here <model_name1>, <model_name2>, ...
are different models under consideration.