Pedro Mercado — accepted AI-conference papers

2026

Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting

Mert Kayaalp, Ali Caner Turkmen, Oleksandr Shchur, Pedro Mercado, Abdul Fatir Ansari, Michael Bohlke-Schneider +1

ICLR 2026poster

Is bigger always better for time series foundation models? With the question in mind, we explore an alternative to training a single, large monolithic model: building a portfolio of smaller, pretrained forecasting models. By applying ensembling or model selection over these portfolios, we achieve co…

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2025

ChronosX: Adapting Pretrained Time Series Models with Exogenous Variables

Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari, Lorenzo Stella, Huibin Shen +7

AISTATS 2025poster

Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, covariates may indicate promotions or peak dates such as holiday seasons that heavily influence demand forecasts. Recent a…

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2023

Coherent Probabilistic Forecasting of Temporal Hierarchies

Syama Sundar Rangapuram, Shubham Kapoor, Rajbir Singh Nirwan, Pedro Mercado, Tim Januschowski, Yuyang Wang +1

AISTATS 2023poster

Forecasts at different time granularities are required in practice for addressing various business problems starting from short-term operational to medium-term tactical and to long-term strategic planning. These forecasting problems are usually treated independently by learning different ML models w…

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2022

PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series

Paul Jeha, Michael Bohlke-Schneider, Pedro Mercado, Shubham Kapoor, Rajbir Singh Nirwan, Valentin Flunkert +2

ICLR 2022poster

Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper we present PSA-GAN, a generative adversarial network (GAN) that generates long time series samples of high quality using…

Cited by 63 Source ↗PDF ↗code ↗Scholar ↗

2021

End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series

Syama Sundar Rangapuram, Lucien D Werner, Konstantinos Benidis, Pedro Mercado, Jan Gasthaus, Tim Januschowski

ICML 2021spotlight

This paper presents a novel approach for hierarchical time series forecasting that produces coherent, probabilistic forecasts without requiring any explicit post-processing reconciliation. Unlike the state-of-the-art, the proposed method simultaneously learns from all time series in the hierarchy an…

Cited by 91 Source ↗PDF ↗Scholar ↗

2019

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs

Pedro Mercado, Francesco Tudisco, Matthias Hein

NeurIPS 2019poster

We study the task of semi-supervised learning on multilayer graphs by taking into account both labeled and unlabeled observations together with the information encoded by each individual graph layer. We propose a regularizer based on the generalized matrix mean, which is a one-parameter family of ma…

Cited by 24 Source ↗PDF ↗code ↗Scholar ↗

2019

Spectral Clustering of Signed Graphs via Matrix Power Means

Pedro Mercado, Francesco Tudisco, Matthias Hein

ICML 2019oral

Signed graphs encode positive (attractive) and negative (repulsive) relations between nodes. We extend spectral clustering to signed graphs via the one-parameter family of Signed Power Mean Laplacians, defined as the matrix power mean of normalized standard and signless Laplacians of positive and ne…

Cited by 54 Source ↗PDF ↗Scholar ↗

2018

The Power Mean Laplacian for Multilayer Graph Clustering

Pedro Mercado, Antoine Gautier, Francesco Tudisco, Matthias Hein

AISTATS 2018poster

Multilayer graphs encode different kind of interactions between the same set of entities. When one wants to cluster such a multilayer graph, the natural question arises how one should merge the information from different layers. We introduce in this paper a one-parameter family of matrix power means…

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2016

Clustering Signed Networks with the Geometric Mean of Laplacians

Pedro Mercado, Francesco Tudisco, Matthias Hein

NeurIPS 2016poster

Signed networks allow to model positive and negative relationships. We analyze existing extensions of spectral clustering to signed networks. It turns out that existing approaches do not recover the ground truth clustering in several situations where either the positive or the negative network struc…

Cited by 58 Source ↗PDF ↗Scholar ↗