Latent Inference-Time Guidance of Time Series Foundation Models

arXiv:2609.38058 · stat.ML, cs.LG, stat.ME · Submitted 2026-09-29 · Read on arXiv

stat.ML, cs.LG, stat.ME

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/Dralliag/opera-python

Terminology

• Foundation Model — 37× in this paper · explained in 3 episodes
• Foundation Models — 30× in this paper · explained in 7 episodes
• Transformer — 12× in this paper · explained in 2 episodes
• Latent Space — 8× in this paper · explained in 7 episodes
• Mixture of Experts — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Activation Function — 6× in this paper · explained in 1 episode
• Benchmark — 6× in this paper · explained in 2 episodes
• In-Context Learning — 5× in this paper · explained in 4 episodes
• Time Series Forecasting — 5× in this paper · explained in 3 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Generative AI — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Latent Representations — 2× in this paper · explained in 3 episodes
• Mean Absolute Error — 2× in this paper
• Reconstruction Error — 2× in this paper · explained in 2 episodes
• Root Mean Square Error — 2× in this paper
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Time Series Foundation Models (TSFMs) — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic Evaluation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multi-Layered Perceptron — 1× in this paper
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Structured Nonlinear Independent Component Analysis — 1× in this paper
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Variational Auto-Encoders — 1× in this paper

Sources

• Chronos-2: From Univariate to Universal Forecasting
• Layer Normalization
• Conversational Time Series Foundation Models: Towards Explainable and Effective Forecasting
• Importance sampling for online variational learning
• TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
• No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves
• Moirai 2.0: When Less Is More for Time Series Forecasting
• Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
• Brittlebench: Quantifying LLM robustness via prompt sensitivity

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