Image de couverture de livre

Towards Foundation Time Series Model: To Synthesize Or Not To Synthesize?

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Kuvshinova, Kseniia, Tsymboi, Olga, Kostromina, Alina, Simakov, Dmitry, Kovtun, Elizaveta
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Tsururu: A Python-based Time Series Forecasting Strategies Library
par: Kostromina, Alina, et autres
Publié: (2025)

Sparse and Transferable Universal Singular Vectors Attack
par: Kuvshinova, Kseniia, et autres
Publié: (2024)

Hiding Backdoors within Event Sequence Data via Poisoning Attacks
par: Ermilova, Alina, et autres
Publié: (2023)

Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention
par: Kovtun, Elizaveta, et autres
Publié: (2023)

PINE: Pipeline for Important Node Exploration in Attributed Networks
par: Kovtun, Elizaveta, et autres
Publié: (2025)

Synthesize, Partition, then Adapt: Eliciting Diverse Samples from Foundation Models
par: Wen, Yeming, et autres
Publié: (2024)

Towards Long-Context Time Series Foundation Models
par: Żukowska, Nina, et autres
Publié: (2024)

Towards Foundation Models for Critical Care Time Series
par: Burger, Manuel, et autres
Publié: (2024)

Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data
par: Shi, Yucheng, et autres
Publié: (2025)

DAM: Towards A Foundation Model for Time Series Forecasting
par: Darlow, Luke, et autres
Publié: (2024)

Kairos: Toward Adaptive and Parameter-Efficient Time Series Foundation Models
par: Feng, Kun, et autres
Publié: (2025)

Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
par: Liu, Qingxiang, et autres
Publié: (2024)

Towards Synthesizing Normative Data for Cognitive Assessments Using Generative Multimodal Large Language Models
par: Yan, Victoria, et autres
Publié: (2025)

Towards Neural Scaling Laws for Time Series Foundation Models
par: Yao, Qingren, et autres
Publié: (2024)

How Foundational are Foundation Models for Time Series Forecasting?
par: Karaouli, Nouha, et autres
Publié: (2025)

Evaluating Time Series Foundation Models on Noisy Periodic Time Series
par: Gupta, Syamantak Datta
Publié: (2025)

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples
par: Li, Zuqing, et autres
Publié: (2025)

Towards Self-Supervised Foundation Models for Critical Care Time Series
par: Jagd, Katja Naasunnguaq, et autres
Publié: (2025)

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
par: Rasul, Kashif, et autres
Publié: (2023)

Towards Synthesizing Twelve-Lead Electrocardiograms from Two Asynchronous Leads
par: Jo, Yong-Yeon, et autres
Publié: (2021)

MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling
par: Naour, Etienne Le, et autres
Publié: (2025)

Federated Foundation Models on Heterogeneous Time Series
par: Chen, Shengchao, et autres
Publié: (2024)

On the Internal Semantics of Time-Series Foundation Models
par: Pandey, Atharva, et autres
Publié: (2025)

Universal Redundancies in Time Series Foundation Models
par: Bao, Anthony, et autres
Publié: (2026)

Smooth Flow Matching for Synthesizing Functional Data
par: Tan, Jianbin, et autres
Publié: (2025)

Beyond Inference-Time Search: Reinforcement Learning Synthesizes Reusable Solvers
par: Massoudi, Soheyl, et autres
Publié: (2026)

Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models
par: Zhong, Yuan, et autres
Publié: (2024)

TimeFound: A Foundation Model for Time Series Forecasting
par: Xiao, Congxi, et autres
Publié: (2025)

Are Time-Indexed Foundation Models the Future of Time Series Imputation?
par: Naour, Etienne Le, et autres
Publié: (2025)

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

Exploring Representations and Interventions in Time Series Foundation Models
par: Wiliński, Michał, et autres
Publié: (2024)

SEMPO: Lightweight Foundation Models for Time Series Forecasting
par: He, Hui, et autres
Publié: (2025)

Are Time Series Foundation Models Susceptible to Catastrophic Forgetting?
par: Karaouli, Nouha, et autres
Publié: (2025)

AI Foundation Model for Time Series with Innovations Representation
par: Tong, Lang, et autres
Publié: (2025)

Accelerating Time Series Foundation Models with Speculative Decoding
par: Subbaraman, Pranav, et autres
Publié: (2025)

Investigating Compositional Reasoning in Time Series Foundation Models
par: Potosnak, Willa, et autres
Publié: (2025)

ClimateGPT: Towards AI Synthesizing Interdisciplinary Research on Climate Change
par: Thulke, David, et autres
Publié: (2024)

Towards Foundation Auto-Encoders for Time-Series Anomaly Detection
par: González, Gastón García, et autres
Publié: (2025)

Synthesizing Realistic Test Data without Breaking Privacy
par: Plein, Laura, et autres
Publié: (2026)

Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions
par: Dutta, Utsav, et autres
Publié: (2025)