Cover Image

One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Luetto, Simone, Garuti, Fabrizio, Sangineto, Enver, Forni, Lorenzo, Cucchiara, Rita
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Diffusion Transformers for Tabular Data Time Series Generation
by: Garuti, Fabrizio, et al.
Published: (2025)

Causal Graphical Models for Vision-Language Compositional Understanding
by: Parascandolo, Fiorenzo, et al.
Published: (2024)

BFS-PO: Best-First Search for Large Reasoning Models
by: Parascandolo, Fiorenzo, et al.
Published: (2026)

TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
by: Suh, Namjoon, et al.
Published: (2024)

Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
by: Dobhal, Umang, et al.
Published: (2026)

Interpretable Pre-Trained Transformers for Heart Time-Series Data
by: Davies, Harry J., et al.
Published: (2024)

ADAPTive Input Training for Many-to-One Pre-Training on Time-Series Classification
by: Quinlan, Paul, et al.
Published: (2026)

Unified Training of Universal Time Series Forecasting Transformers
by: Woo, Gerald, et al.
Published: (2024)

A Second-Order Perspective on Model Compositionality and Incremental Learning
by: Porrello, Angelo, et al.
Published: (2024)

On-device AI: Quantization-aware Training of Transformers in Time-Series
by: Ling, Tianheng, et al.
Published: (2024)

DenoGrad: A Gradient-Based Framework for Data Refinement in Tabular and Time-Series Learning
by: Alonso-Ramos, J. Javier, et al.
Published: (2025)

Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
by: Yang, Yuxuan, et al.
Published: (2025)

Mamba or Transformer for Time Series Forecasting? Mixture of Universals (MoU) Is All You Need
by: Peng, Sijia, et al.
Published: (2024)

Time-Prompt: Integrated Heterogeneous Prompts for Unlocking LLMs in Time Series Forecasting
by: Wang, Zesen, et al.
Published: (2025)

Zero-shot Multivariate Time Series Forecasting Using Tabular Prior Fitted Networks
by: Jayawardhana, Mayuka, et al.
Published: (2026)

HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
by: Wang, Tan, et al.
Published: (2025)

Predicting Estimated Times of Restoration for Electrical Outages Using Longitudinal Tabular Transformers
by: Teja, Bogireddy Sai Prasanna, et al.
Published: (2025)

Optimizing the Training Diet: Data Mixture Search for Robust Time Series Forecasting
by: Pennino, Federico, et al.
Published: (2025)

PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes
by: Zhou, Yiming, et al.
Published: (2026)

Reasoning-Aware Training for Time Series Forecasting
by: Ahamed, Md Atik, et al.
Published: (2026)

Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining
by: Yan, Xu, et al.
Published: (2024)

ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling
by: Chen, Yuqi, et al.
Published: (2024)

TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables
by: Wang, Yuxuan, et al.
Published: (2024)

VARMA-Enhanced Transformer for Time Series Forecasting
by: Song, Jiajun, et al.
Published: (2025)

Transformers and Their Roles as Time Series Foundation Models
by: Wu, Dennis, et al.
Published: (2025)

Toto: Time Series Optimized Transformer for Observability
by: Cohen, Ben, et al.
Published: (2024)

Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
by: Baer, Gregor, et al.
Published: (2025)

Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-Training
by: Baraldi, Lorenzo, et al.
Published: (2023)

RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
by: Mou, Xudong, et al.
Published: (2025)

Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing
by: Lee, Sewoong, et al.
Published: (2023)

OLMA: One Loss for More Accurate Time Series Forecasting
by: Shi, Tianyi, et al.
Published: (2025)

Test-Time Training Undermines Safety Guardrails
by: Antonelli, Simone, et al.
Published: (2026)

UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
by: Liu, Juncheng, et al.
Published: (2024)

TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model
by: Cao, Defu, et al.
Published: (2024)

A Survey on Time-Series Pre-Trained Models
by: Ma, Qianli, et al.
Published: (2023)

Training and Evaluating Causal Forecasting Models for Time-Series
by: Crasson, Thomas, et al.
Published: (2024)

Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting
by: Xu, Jingjing, et al.
Published: (2024)

Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
by: Wu, Shunyu, et al.
Published: (2025)

TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning
by: Zhang, Junru, et al.
Published: (2025)

Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting
by: Kang, Bong Gyun, et al.
Published: (2024)