Aurora-X: Built for Extreme Time Series Forecasting

arXiv:2609.31038 · cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

• Time Series Forecasting — 29× in this paper · explained in 3 episodes
• Benchmark — 25× in this paper · explained in 2 episodes
• Foundation Model — 13× in this paper · explained in 3 episodes
• Clustering — 12× in this paper · explained in 2 episodes
• Adaptation — 10× in this paper · explained in 1 episode
• Transformer — 9× in this paper · explained in 2 episodes
• Foundation Models — 8× in this paper · explained in 7 episodes
• Probabilistic Forecasting — 8× in this paper · explained in 3 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Flow Matching — 4× in this paper · explained in 8 episodes
• Mixture of Experts — 4× in this paper · explained in 2 episodes
• Test-time Scaling — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 4 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• In-Context Learning — 3× in this paper · explained in 4 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Time Series Foundation Models (TSFMs) — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Adaptive Graph — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Mixture of Experts (MoE) — 1× in this paper · explained in 5 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning
• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
• Chronos-2: From Univariate to Universal Forecasting
• Qwen Technical Report
• From Similarity to Superiority: Channel Clustering for Time Series Forecasting
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Toto 2.0: Time Series Forecasting Enters the Scaling Era
• DeepSeek-V3 Technical Report
• Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
• Sundial: A Family of Highly Capable Time Series Foundation Models
• Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling
• Controllable Video Generation: A Survey
• LiveLight: Real-time Streaming Video Relighting with Interactive Control
• TiRex-2: Generalizing TiRex to Multivariate Data and Streaming
• It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks
• fev-bench: A Realistic Benchmark for Time Series Forecasting
• Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
• Xihe: Scalable Zero-Shot Time Series Learner Via Hierarchical Interleaved Block Attention
• FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting
• TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentation

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks