Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures

Kin Gutierrez Olivares, Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao

Keywords: Hierarchical Forecasting, Neural Networks, Poisson Mixture

TL;DR: We introduce the Poisson Mixture Mesh likelihood for hierarchical forecasting applications

Abstract: Hierarchical forecasting problems arise when time series compose a group structure that naturally defines aggregation and disaggregation coherence constraints for the predictions. In this work, we explore a new forecast representation, the Poisson Mixture Mesh (PMM), that can produce probabilistic, coherent predictions; it is compatible with the neural forecasting innovations, and defines simple aggregation and disaggregation rules capable of accommodating hierarchical structures, unknown during its optimization. We perform an empirical evaluation to compare the PMM to other methods on Australian domestic tourism data.

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2110.13179/code)