Metadata-Version: 2.1
Name: probabilistic-reconciliation
Version: 0.0.4
Summary: Probabilistic reconciliation of time series forecasts
Project-URL: homepage, https://github.com/dirmeier/reconcile
Author-email: Simon Dirmeier <sfyrbnd@pm.me>
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: forecasting,hierarchical time series,probabilistic reconciliation,timeseries
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.9
Requires-Dist: blackjax-nightly>=0.9.6.post127
Requires-Dist: chex>=0.1.5
Requires-Dist: distrax>=0.1.2
Requires-Dist: flax>=0.6.1
Requires-Dist: optax>=0.1.3
Requires-Dist: pandas>=1.5.1
Description-Content-Type: text/markdown

# reconcile

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> Probabilistic reconciliation of time series forecasts

## About

Reconcile implements probabilistic time series forecast reconciliation methods introduced in

1) Zambon, Lorenzo, Dario Azzimonti, and Giorgio Corani. ["Probabilistic reconciliation of forecasts via importance sampling."](https://doi.org/10.48550/arXiv.2210.02286) arXiv preprint arXiv:2210.02286 (2022).
2) Panagiotelis, Anastasios, et al. ["Probabilistic forecast reconciliation: Properties, evaluation and score optimisation."](https://doi.org/10.1016/j.ejor.2022.07.040) European Journal of Operational Research (2022).

The package implements

- methods to compute summing/aggregation matrices for grouped and hierarchical time series,
- an abstract base forecasting class,
- reconciliation methods for forecasts based on sampling and optimization

An example application can be found in `examples/reconciliation.py` and
a **case study on probabilistic forecast reconciliation of stock index data**
can be found [here](https://dirmeier.github.io/etudes/probabilistic_reconciliation.html).

## Installation

Make sure to have a working `JAX` installation. Depending whether you want to use CPU/GPU/TPU,
please follow [these instructions](https://github.com/google/jax#installation).

To install the package from PyPI, call:

```bash
pip install probabilistic-reconciliation
```

To install the latest GitHub <RELEASE>, just call the following on the
command line:

```bash
pip install git+https://github.com/dirmeier/reconcile@<RELEASE>
```

## Author

Simon Dirmeier <a href="mailto:sfyrbnd @ pm me">sfyrbnd @ pm me</a>
