no code implementations • 22 Sep 2023 • Willa Potosnak, Cristian Challu, Kin G. Olivares, Artur Dubrawski
Our global-local architecture improves over patient-specific models by 9. 2-14. 6%.
1 code implementation • 11 May 2023 • Kin G. Olivares, David Luo, Cristian Challu, Stefania La Vattiata, Max Mergenthaler, Artur Dubrawski
Large collections of time series data are often organized into hierarchies with different levels of aggregation; examples include product and geographical groupings.
1 code implementation • 7 Jul 2022 • Kin G. Olivares, Federico Garza, David Luo, Cristian Challú, Max Mergenthaler, Souhaib Ben Taieb, Shanika L. Wickramasuriya, Artur Dubrawski
Large collections of time series data are commonly organized into structures with different levels of aggregation; examples include product and geographical groupings.
4 code implementations • 30 Jan 2022 • Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski
Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems.
no code implementations • 25 Oct 2021 • Kin G. Olivares, O. Nganba Meetei, Ruijun Ma, Rohan Reddy, Mengfei Cao, Lee Dicker
Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed.
no code implementations • 7 Jun 2021 • Cristian Challu, Kin G. Olivares, Gus Welter, Artur Dubrawski
We validate our proposed method, DMIDAS, on high-frequency healthcare and electricity price data with long forecasting horizons (~1000 timestamps) where we improve the prediction accuracy by 5% over state-of-the-art models, reducing the number of parameters of NBEATS by nearly 70%.
2 code implementations • 12 Apr 2021 • Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz, Rafał Weron, Artur Dubrawski
We extend the neural basis expansion analysis (NBEATS) to incorporate exogenous factors.