Post-Training in Time Series Foundation Models: A Unifying Framework
cs.LG, cs.AI
Submitted: 2026-07-22
Updated: 2026-09-15
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Foundation models for time series forecasting: Application in conformal prediction
- Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting
- Chronos-2: From Univariate to Universal Forecasting
- The UEA multivariate time series classification archive, 2018
- Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
- STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter
- ProbFM: Probabilistic Time Series Foundation Model with Uncertainty Decomposition
- AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
- Mantis: Lightweight Foundation Model for Time Series Classification
- TimeGPT-1
- Time-Series Foundation AI Model for Value-at-Risk Forecasting
- Foundation Time-Series AI Model for Realized Volatility Forecasting
- Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models
- Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction
- Distilling the Knowledge in a Neural Network
- SeqFusion: Sequential Fusion of Pre-Trained Models for Zero-Shot Time-Series Forecasting
- Gemma 3 Technical Report
- Segment Anything
- Foundation Models for Time Series: A Survey
- LLM Post-Training: A Deep Dive into Reasoning Large Language Models
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