Hierarchically Coherent Multivariate Mixture Networks

• URL: http://arxiv.org/abs/2305.07089v2
• Date: Mon, 16 Oct 2023 18:37:06 GMT
• Title: Hierarchically Coherent Multivariate Mixture Networks
• Authors: Kin G. Olivares, David Luo, Cristian Challu, Stefania La Vattiata, Max Mergenthaler, Artur Dubrawski
• Abstract summary: Probabilistic coherent forecasting is tasked to produce forecasts consistent across levels of aggregation. We optimize the networks with a composite likelihood objective, allowing us to capture time series' relationships. Our approach demonstrates 13.2% average accuracy improvements on most datasets compared to state-of-the-art baselines.
• Score: 11.40498954142061
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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