Nicolas Chapados

Yuriy Nevmyvaka

Irina Rish

Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (voir plus)t and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.

2023-10-12

ArXiv (prépublication)

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Arian Khorasani

George Adamopoulos

Rishika Bhagwatkar

Marin Bilovs

Hena Ghonia

Nadhir Hassen

Anderson Schneider

Sahil Garg

Yuriy Nevmyvaka

Irina Rish

2023-10-12

ArXiv (prépublication)

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series

Arjun Ashok

Étienne Marcotte

Valentina Zantedeschi

We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including fore… (voir plus)casting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS.

2023-10-02

ArXiv (prépublication)

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts

Étienne Marcotte

Valentina Zantedeschi

Multivariate probabilistic time series forecasts are commonly evaluated via proper scoring rules, i.e., functions that are minimal in expect… (voir plus)ation for the ground-truth distribution. However, this property is not sufficient to guarantee good discrimination in the non-asymptotic regime. In this paper, we provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. Through a power analysis, we identify the"region of reliability"of a scoring rule, i.e., the set of practical conditions where it can be relied on to identify forecasting errors. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions, and we gauge the generalizability of our findings to real-world tasks with an application to an electricity production problem. Our results reveal critical shortcomings in the evaluation of multivariate probabilistic forecasts as commonly performed in the literature.

2023-07-03

Proceedings of the 40th International Conference on Machine Learning (publié)

openreview.net

Dynamic Routing and Wavelength Assignment with Reinforcement Learning

Peyman Kafaei

Quentin Cappart

Hamed Pouya

Louis-Martin Rousseau

With the rapid developments in communication systems, and considering their dynamic nature, all-optical networks are becoming increasingly c… (voir plus)omplex. This study proposes a novel method based on deep reinforcement learning for the routing and wavelength assignment problem in all-optical wavelength-decision-multiplexing networks. We consider dynamic incoming requests, in which their arrival and holding times are not known in advance. The objective is to devise a strategy that minimizes the number of rejected packages due to the lack of resources in the long term. We use graph neural networks to capture crucial latent information from the graph-structured input to develop the optimal strategy. The proposed deep reinforcement learning algorithm selects a route and a wavelength simultaneously for each incoming traffic connection as they arrive. The results demonstrate that the learned agent outperforms the methods used in practice and can be generalized on network topologies that did not participate in training.

2023-06-08

INFORMS Journal on Optimization (publié)

Can AI Read the Minds of Corporate Executives?

Zhenzhen Fan

Ruslan Goyenko

Issam Hadj Laradji

Fred Liu

Chengyu Zhang

2023-01-01

SSRN Electronic Journal (publié)

Lag-Llama: Towards Foundation Models for Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Arian Khorasani

George Adamopoulos

Rishika Bhagwatkar

Marin Biloš

Hena Ghonia

N. Hassen

Anderson Schneider

Sahil Garg

Yuriy Nevmyvaka

Irina Rish

Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (voir plus)Llama , a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen “out-of-distribution” time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws [7] to fit and predict model scaling behavior. The open source code is made available at https://github

2023-01-01

arXiv.org (prépublication)

TACTiS: Transformer-Attentional Copulas for Time Series

Étienne Marcotte

The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, t… (voir plus)he practical utility of such estimates is limited by how accurately they quantify predictive uncertainty. In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series. We propose a versatile method, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas. The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training. We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.

2022-02-07

ArXiv (prépublication)

TACTiS: Transformer-Attentional Copulas for Time Series

Étienne Marcotte

The estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. However, t… (voir plus)he practical utility of such estimates is limited by how accurately they quantify predictive uncertainty. In this work, we address the problem of estimating the joint predictive distribution of high-dimensional multivariate time series. We propose a versatile method, based on the transformer architecture, that estimates joint distributions using an attention-based decoder that provably learns to mimic the properties of non-parametric copulas. The resulting model has several desirable properties: it can scale to hundreds of time series, supports both forecasting and interpolation, can handle unaligned and non-uniformly sampled data, and can seamlessly adapt to missing data during training. We demonstrate these properties empirically and show that our model produces state-of-the-art predictions on multiple real-world datasets.

2022-01-01

ICML (publié)

proceedings.mlr.press

Meta-learning framework with applications to zero-shot time-series forecasting

Boris Oreshkin

Dmitri Carpov

Yoshua Bengio

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (voir plus) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

2021-05-18

Proceedings of the AAAI Conference on Artificial Intelligence (publié)

Meta-learning framework with applications to zero-shot time-series forecasting

Boris Oreshkin

Dmitri Carpov

Yoshua Bengio

Can meta-learning discover generic ways of processing time series (TS) from a diverse dataset so as to greatly improve generalization on new… (voir plus) TS coming from different datasets? This work provides positive evidence to this using a broad meta-learning framework which we show subsumes many existing meta-learning algorithms. Our theoretical analysis suggests that residual connections act as a meta-learning adaptation mechanism, generating a subset of task-specific parameters based on a given TS input, thus gradually expanding the expressive power of the architecture on-the-fly. The same mechanism is shown via linearization analysis to have the interpretation of a sequential update of the final linear layer. Our empirical results on a wide range of data emphasize the importance of the identified meta-learning mechanisms for successful zero-shot univariate forecasting, suggesting that it is viable to train a neural network on a source TS dataset and deploy it on a different target TS dataset without retraining, resulting in performance that is at least as good as that of state-of-practice univariate forecasting models.

2020-02-07

ArXiv (prépublication)

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

Boris Oreshkin

Dmitri Carpov