Hierarchical 👑 Forecast

Hierarchical forecasting with statistical and econometric methods

HierarchicalForecast offers a collection of reconciliation methods, including BottomUp, TopDown, MiddleOut, MinTrace and ERM.

• Classic reconciliation methods:
  • BottomUp: Simple addition to the upper levels.
  • TopDown: Distributes the top levels forecasts trough the hierarchies.
• Alternative reconciliation methods:
  • MiddleOut: It anchors the base predictions in a middle level. The levels above the base predictions use the bottom-up approach, while the levels below use a top-down.
  • MinTrace: Minimizes the total forecast variance of the space of coherent forecasts, with the Minimum Trace reconciliation.
  • ERM: Optimizes the reconciliation matrix minimizing an L1 regularized objective.

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Short: We want to contribute to the ML field by providing reliable baselines and benchmarks for hierarchical forecasting task in industry and academia. Here's the complete paper.

Verbose: HierarchicalForecast integrates publicly available processed datasets, evaluation metrics, and a curated set of standard statistical baselines. In this library we provide usage examples and references to extensive experiments where we showcase the baseline's use and evaluate the accuracy of their predictions. With this work, we hope to contribute to Machine Learning forecasting by bridging the gap to statistical and econometric modeling, as well as providing tools for the development of novel hierarchical forecasting algorithms rooted in a thorough comparison of these well-established models. We intend to continue maintaining and increasing the repository, promoting collaboration across the forecasting community.

You can install the released version of HierarchicalForecast from the Python package index with:

pip install hierarchicalforecast

(Installing inside a python virtualenvironment or a conda environment is recommended.)

Also you can install the released version of HierarchicalForecast from conda with:

conda install -c conda-forge hierarchicalforecast

(Installing inside a python virtualenvironment or a conda environment is recommended.)

If you want to make some modifications to the code and see the effects in real time (without reinstalling), follow the steps below:

git clone https://github.com/Nixtla/hierarchicalforecast.git
cd hierarchicalforecast
pip install -e .

The following example needs statsforecast and datasetsforecast as additional packages. If not installed, install it via your preferred method, e.g. pip install statsforecast datasetsforecast. The datasetsforecast library allows us to download hierarhical datasets and we will use statsforecast to compute base forecasts to be reconciled.

You can open this example in Colab

import numpy as np
import pandas as pd

#obtain hierarchical dataset
from datasetsforecast.hierarchical import HierarchicalData
#obtain hierarchical reconciliation methods and evaluation
from hierarchicalforecast.core import HierarchicalReconciliation
from hierarchicalforecast.evaluation import HierarchicalEvaluation
from hierarchicalforecast.methods import BottomUp, TopDown, MiddleOut
# compute base forecast no coherent
from statsforecast.core import StatsForecast
from statsforecast.models import AutoARIMA, Naive

# Load TourismSmall dataset
Y_df, S, tags = HierarchicalData.load('./data', 'TourismSmall')
Y_df['ds'] = pd.to_datetime(Y_df['ds'])

#split train/test sets
Y_df_test = Y_df.groupby('unique_id').tail(12)
Y_df_train = Y_df.drop(Y_df_test.index)
Y_df_test = Y_df_test.set_index('unique_id')
Y_df_train = Y_df_train.set_index('unique_id')

# Compute base auto-ARIMA predictions
fcst = StatsForecast(df=Y_df_train, models=[AutoARIMA(season_length=12), Naive()], freq='M', n_jobs=-1)
Y_hat_df = fcst.forecast(h=12)

# Reconcile the base predictions
reconcilers = [
    BottomUp(),
    TopDown(method='forecast_proportions'),
    MiddleOut(level='Country/Purpose/State', top_down_method='forecast_proportions')
]
hrec = HierarchicalReconciliation(reconcilers=reconcilers)
Y_rec_df = hrec.reconcile(Y_hat_df, Y_df_train, S, tags)

def mse(y, y_hat):
    return np.mean((y-y_hat)**2)

evaluator = HierarchicalEvaluation(evaluators=[mse])
evaluator.evaluate(Y_h=Y_rec_df, Y_test=Y_df_test, 
                   tags=tags, benchmark='Naive')

This project is licensed under the MIT License - see the LICENSE file for details.

In the R ecosystem, we recommend checking out fable, and the now-retired hts. In Python we want to acknowledge the following libraries hiere2e, sktime, darts, pyhts, scikit-hts.

This work is highly influenced by the fantastic work of previous contributors and other scholars who previously proposed the reconciliation methods presented here. We want to highlight the work of Rob Hyndman, George Athanasopoulos, Shanika L. Wickramasuriya, Souhaib Ben Taieb, and Bonsoo Koo. For a full reference link, please visit the Reference section of this paper. We encourage users to explore this literature review.

If you enjoy or benefit from using these Python implementations, a citation to this hierarchical forecasting reference paper will be greatly appreciated.

@article{olivares2022hierarchicalforecast,
    author    = {Kin G. Olivares and
                 Federico Garza and 
                 David Luo and 
                 Cristian Challú and
                 Max Mergenthaler and
                 Artur Dubrawski},
    title     = {HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python},
    journal   = {Computing Research Repository},
    volume    = {abs/2207.03517},
    year      = {2022},
    url       = {https://arxiv.org/abs/2207.03517},
    archivePrefix = {arXiv}
}