Long Range Probabilistic Forecasting in Time-Series using High Order Statistics

This is the code produced as part of the paper Long Range Probabilistic Forecasting in Time-Series using High Order Statistics

Long Range Probabilistic Forecasting in Time-Series using High Order Statistics.

Prathamesh Deshpande and Sunita Sarawagi. arXiv:2111.03394v1.

How to work with Command Line Arguments?

• If an optional argument is not passed, it's value will be extracted from configuration specified in the file main.py (based on dataset_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.

Command Line Arguments Information

Datasets

All the datasets can be found here.

Add the dataset files/directories in data directory before running the code.

Output files

Targets and Forecasts

Following output files are stored in the <output_dir>/<dataset_name>/ directory.

Metrics

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.