Learning the Context of Errors: Black-Box Online Adaptation of Time Series Foundation Models
cs.LG
Submitted: 2026-06-12
Updated: 2026-10-03
Comments: Accepted by NeurIPS 2026
Code: https://github.com/Fifthky/ORCA
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting
- GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
- Chronos-2: From Univariate to Universal Forecasting
- Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context
- Position: Universal Time Series Foundation Models Rest on a Category Error
- Maintaining Plasticity in Deep Continual Learning
- TimeGPT-1
- When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
- TEFL: Prediction-Residual-Guided Rolling Forecasting for Multi-Horizon Time Series
- Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
- Adapting Time Series Foundation Models through Data Mixtures
- Moirai 2.0: When Less Is More for Time Series Forecasting
- Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
- Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
- A Mamba Foundation Model for Time Series Forecasting
- Rethinking Evaluation in the Era of Time Series Foundation Models: (Un)known Information Leakage Challenges
- A Survey of Deep Learning and Foundation Models for Time Series Forecasting
- Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
- fev-bench: A Realistic Benchmark for Time Series Forecasting
- Contextual Agentic Memory is a Memo, Not True Memory
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