Cover Image

Shapelets-Enriched Selective Forecasting using Time Series Foundation Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tomar, Shivani, Tirupathi, Seshu, Daly, Elizabeth, Dusparic, Ivana
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bridging the High-Frequency Data Gap: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
by: Khanal, Subina, et al.
Published: (2026)

Optimistic Exploration for Risk-Averse Constrained Reinforcement Learning
by: McCarthy, James, et al.
Published: (2025)

Domain Adaptation for Time series Transformers using One-step fine-tuning
by: Khanal, Subina, et al.
Published: (2024)

Pre-Hoc Predictions in AutoML: Leveraging LLMs to Enhance Model Selection and Benchmarking for Tabular datasets
by: Belkhiter, Yannis, et al.
Published: (2025)

Context-Aware Model-Based Reinforcement Learning for Autonomous Racing
by: Moustafa, Emran Yasser, et al.
Published: (2025)

ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies
by: Gajcin, Jasmina, et al.
Published: (2024)

Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
by: Huang, Qi, et al.
Published: (2024)

GAF-Guard: An Agentic Framework for Risk Management and Governance in Large Language Models
by: Tirupathi, Seshu, et al.
Published: (2025)

ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification
by: Le, Xuan-May, et al.
Published: (2024)

Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification
by: Lee, Jiseok, et al.
Published: (2024)

FedST: Secure Federated Shapelet Transformation for Time Series Classification
by: Liang, Zhiyu, et al.
Published: (2023)

ShapeCond: Fast Shapelet-Guided Dataset Condensation for Time Series Classification
by: Peng, Sijia, et al.
Published: (2026)

A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning
by: Liang, Zhiyu, et al.
Published: (2023)

TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis
by: Liang, Zhiyu, et al.
Published: (2024)

ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models
by: Huang, Bosong, et al.
Published: (2025)

Actor-Enriched Time Series Forecasting of Process Performance
by: Leribaux, Aurelie, et al.
Published: (2025)

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

Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
by: Belkhiter, Yannis, et al.
Published: (2025)

TREX: Trajectory Explanations for Multi-Objective Reinforcement Learning
by: Rajapakse, Dilina, et al.
Published: (2026)

TimeFound: A Foundation Model for Time Series Forecasting
by: Xiao, Congxi, et al.
Published: (2025)

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

SEMPO: Lightweight Foundation Models for Time Series Forecasting
by: He, Hui, et al.
Published: (2025)

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

Time Series Foundation Models for Process Model Forecasting
by: Yu, Yongbo, et al.
Published: (2025)

Transfer Learning with Foundational Models for Time Series Forecasting using Low-Rank Adaptations
by: Germán-Morales, M., et al.
Published: (2024)

Lightweight Online Adaption for Time Series Foundation Model Forecasts
by: Lee, Thomas L., et al.
Published: (2025)

DAM: Towards A Foundation Model for Time Series Forecasting
by: Darlow, Luke, et al.
Published: (2024)

Adapting the Behavior of Reinforcement Learning Agents to Changing Action Spaces and Reward Functions
by: de la Rosa, Raul, et al.
Published: (2026)

Heterogeneous Relationships of Subjects and Shapelets for Semi-supervised Multivariate Series Classification
by: Du, Mingsen, et al.
Published: (2024)

Distilling Time Series Foundation Models for Efficient Forecasting
by: Li, Yuqi, et al.
Published: (2026)

A Mamba Foundation Model for Time Series Forecasting
by: Ma, Haoyu, et al.
Published: (2024)

Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
by: Hertel, Matthias, et al.
Published: (2026)

A Survey of Deep Learning and Foundation Models for Time Series Forecasting
by: Miller, John A., et al.
Published: (2024)

Efficient Model Selection for Time Series Forecasting via LLMs
by: Wei, Wang, et al.
Published: (2025)

Generalisation Bounds of Zero-Shot Economic Forecasting using Time Series Foundation Models
by: Jetwiriyanon, Jittarin, et al.
Published: (2025)

Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
by: Wang, Wenlong, et al.
Published: (2024)

Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
by: Liu, Qingxiang, et al.
Published: (2024)

Continual Reinforcement Learning for Cyber-Physical Systems: Lessons Learned and Open Challenges
by: Nolle, Kim N., et al.
Published: (2025)

Selective Learning for Deep Time Series Forecasting
by: Fu, Yisong, et al.
Published: (2025)

Time-Series Foundation Models for Forecasting Soil Moisture Levels in Smart Agriculture
by: Deforce, Boje, et al.
Published: (2024)