Cover Image

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Benechehab, Abdelhakim, Feofanov, Vasilii, Paolo, Giuseppe, Thomas, Albert, Filippone, Maurizio, Kégl, Balázs
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning
by: Benechehab, Abdelhakim, et al.
Published: (2024)

Deep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning
by: Benechehab, Abdelhakim, et al.
Published: (2024)

TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning
by: Paolo, Giuseppe, et al.
Published: (2025)

From Data to Rewards: a Bilevel Optimization Perspective on Maximum Likelihood Estimation
by: Benechehab, Abdelhakim, et al.
Published: (2025)

Zero-shot Model-based Reinforcement Learning using Large Language Models
by: Benechehab, Abdelhakim, et al.
Published: (2024)

User-friendly Foundation Model Adapters for Multivariate Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2024)

LLMs as In-Context Meta-Learners for Model and Hyperparameter Selection
by: Hili, Youssef Attia El, et al.
Published: (2025)

Measuring Pre-training Data Quality without Labels for Time Series Foundation Models
by: Wen, Songkang, et al.
Published: (2024)

Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series
by: Liu, Mingzhu, et al.
Published: (2024)

UTICA: Multi-Objective Self-Distllation Foundation Model Pretraining for Time Series Classification
by: Moakher, Yessin, et al.
Published: (2026)

Leveraging Generic Time Series Foundation Models for EEG Classification
by: Gnassounou, Théo, et al.
Published: (2025)

SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention
by: Ilbert, Romain, et al.
Published: (2024)

Guided Safe Shooting: model based reinforcement learning with safety constraints
by: Paolo, Giuseppe, et al.
Published: (2022)

DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models
by: Li, Jinpeng, et al.
Published: (2026)

Demand Forecasting for Platelet Usage: from Univariate Time Series to Multivariate Models
by: Motamedi, Maryam, et al.
Published: (2021)

Univariate to Multivariate: LLMs as Zero-Shot Predictors for Time-Series Forecasting
by: Madarasingha, Chamara, et al.
Published: (2025)

Univariate Channel Fusion for Multivariate Time Series Classification
by: Moro, Fernando, et al.
Published: (2026)

Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
by: Ilbert, Romain, et al.
Published: (2024)

Can LLMs predict the convergence of Stochastic Gradient Descent?
by: Zekri, Oussama, et al.
Published: (2024)

Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification
by: Feofanov, Vasilii, et al.
Published: (2025)

Time Series Foundation Models for Multivariate Financial Time Series Forecasting
by: Marconi, Ben A.
Published: (2025)

Probabilistic Circuits for Irregular Multivariate Time Series Forecasting
by: Klötergens, Christian, et al.
Published: (2026)

Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas
by: Huk, David, et al.
Published: (2026)

Multivariate Probabilistic Time Series Forecasting with Correlated Errors
by: Zheng, Vincent Zhihao, et al.
Published: (2024)

MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
by: Feofanov, Vasilii, et al.
Published: (2026)

Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction
by: Hassan, Atif, et al.
Published: (2025)

Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers
by: Roschmann, Simon, et al.
Published: (2025)

CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
by: Xie, Shifeng, et al.
Published: (2025)

Winner-takes-all for Multivariate Probabilistic Time Series Forecasting
by: Cortés, Adrien, et al.
Published: (2025)

Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
by: Odonnat, Ambroise, et al.
Published: (2023)

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
by: Rasul, Kashif, et al.
Published: (2023)

Channel-aware Contrastive Conditional Diffusion for Multivariate Probabilistic Time Series Forecasting
by: Li, Siyang, et al.
Published: (2024)

Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models
by: Park, Young Jin, et al.
Published: (2025)

Adapting Time Series Foundation Models through Data Mixtures
by: Lee, Thomas L., et al.
Published: (2026)

A call for embodied AI
by: Paolo, Giuseppe, et al.
Published: (2024)

Hybrid Quantum Neural Network for Multivariate Clinical Time Series Forecasting
by: Iele, Irene, et al.
Published: (2026)

LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
by: Roger, Alexis, et al.
Published: (2026)

Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study
by: Leites, José, et al.
Published: (2024)

Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance
by: Grosnit, Antoine, et al.
Published: (2024)

How Foundational are Foundation Models for Time Series Forecasting?
by: Karaouli, Nouha, et al.
Published: (2025)