Hierarchically Coherent Multivariate Mixture Networks
Abstract
Large collections of time series data are often organized into hierarchies with different levels of aggregation; examples include product and geographical groupings. Probabilistic coherent forecasting is tasked to produce forecasts consistent across levels of aggregation. In this study, we propose to augment neural forecasting architectures with a coherent multivariate mixture output. We optimize the networks with a composite likelihood objective, allowing us to capture time series' relationships while maintaining high computational efficiency. Our approach demonstrates 13.2% average accuracy improvements on most datasets compared to state-of-the-art baselines. We conduct ablation studies of the framework components and provide theoretical foundations for them. To assist related work, the code is available at this https://github.com/Nixtla/neuralforecast.
Cite
@article{arxiv.2305.07089,
title = {Hierarchically Coherent Multivariate Mixture Networks},
author = {Kin G. Olivares and David Luo and Cristian Challu and Stefania La Vattiata and Max Mergenthaler and Artur Dubrawski},
journal= {arXiv preprint arXiv:2305.07089},
year = {2023}
}