PanelSplit is a Python package designed to facilitate time series cross-validation when working with multiple entities (aka panel data). This tool is useful for handling panel data in various stages throughout the data pipeline, including feature engineering, hyper-parameter tuning, and model estimation.
You can install PanelSplit using pip:
pip install panelsplitTo read the documentation, visit here.
import pandas as pd
from panelsplit import PanelSplit
# Generate example data
num_countries = 2
years = range(2001, 2004)
num_years = len(years)
data_dict = {
'country_id': [c for c in range(1, num_countries + 1) for _ in years],
'year': [year for _ in range(num_countries) for year in years],
'y': np.random.normal(0, 1, num_countries * num_years),
'x1': np.random.normal(0, 1, num_countries * num_years),
'x2': np.random.normal(0, 1, num_countries * num_years)
}
panel_data = pd.DataFrame(data_dict)
panel_split = PanelSplit(periods = panel_data.year, n_splits =2)
splits = panel_split.split()
for train_idx, test_idx in splits:
print("Train:"); display(panel_data.loc[train_idx])
print("Test:"); display(panel_data.loc[test_idx])For more examples and detailed usage instructions, refer to the examples directory in this repository. Also feel free to check out an article I wrote about PanelSplit.
Work on panelsplit started at EconAI in December 2023 and has been under active development since then.
Contributions to PanelSplit are welcome! If you encounter any issues or have suggestions for improvements, please feel free to open an issue or submit a pull request on GitHub.
This project is licensed under the MIT License - see the LICENSE file for details.