Time Series

Lag-Llama: A Foundation Model for Probabilistic Time Series Forecasting

In this work, we present Lag-Llama, a general-purpose probabilistic time series forecasting model trained on a large collection of time …

Kashif Rasul, Arjun Ashok, Marin Bilos, Andrew Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Hena Ghonia, Nadhir Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, Irina Rish

Workshop at the Neural Information Processing Systems (NeurIPS), 2023.

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts

Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i.e., functions that are minimal in …

Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin, Nicolas Chapados

International Conference on Machine Learning (ICML), 2023.

TACTiS: Transformer-Attentional Copulas for Time Series

The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. …

Alexandre Drouin, Étienne Marcotte, Nicolas Chapados

Montreal AI Symposium (MAIS), 2022.

TACTiS: Transformer-Attentional Copulas for Time Series

The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. …

Alexandre Drouin, Étienne Marcotte, Nicolas Chapados

International Conference on Machine Learning (ICML), 2022.

Deconfounding Dynamic Treatment Regimes

Counterfactual prediction under sequences of actions is a fundamental problem in decision-making. Existing methods in causal inference …

David Berger, Alexandre Drouin, Alexandre Lacoste, Laurent Charlin

SSC, 2022.

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture …

Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio

International Conference on Learning Representations (ICLR), 2020.

Meta-Learning Framework with Applications to Zero-Shot Time Series Forecasting

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization …

Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio

International Conference on Learning Representations (ICLR), 2019.