Hierarchical time series reconciliation
Project description
hierTS 
Hierachical Time Series (hierTS) is a lightweight package that offers hierarchical forecasting reconciliation techniques to Python users.
For more details, read the docs or check out the examples.
Reference
The reconciliation methods that are currently in place are based on:
- Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. Journal of the American Statistical Association, 114(526), 804-819.
- Ben Taieb, Souhaib, and Bonsoo Koo (2019). ‘Regularized Regression for Hierarchical Forecasting Without Unbiasedness Conditions’. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1337–47. Anchorage AK USA: ACM, 2019. https://doi.org/10.1145/3292500.3330976.
License
This project is licensed under the terms of the Apache 2.0 license.
Acknowledgements
This project was developed by Airlab Amsterdam.
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