NanoForecast v0.5: Competitive Time Series Forecasting Through Training Pipeline Optimization

arXiv:2609.31669 · cs.LG · Submitted 2026-09-15 · Read on arXiv

cs.LG

Submitted: 2026-09-15

Updated: 2026-09-15

Code: https://github.com/eulogik/NanoForecasthttps:

Terminology

• Transformer — 16× in this paper · explained in 2 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Time Series Forecasting — 10× in this paper · explained in 3 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Foundation Model — 9× in this paper · explained in 3 episodes
• Foundation Models — 7× in this paper · explained in 7 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Quantization — 3× in this paper · explained in 12 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• In International Conference on Machine Learning — 2× in this paper
• Mean Absolute Scaled Error — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Transactions on Machine Learning Research — 1× in this paper

Sources

• Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
• Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting
• Unified Training of Universal Time Series Forecasting Transformers
• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
• FrAug: Frequency Domain Augmentation for Time Series Forecasting
• GLU Variants Improve Transformer

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