Correct then Forecast: Observer State-Space Models for Time Series Forecasting

arXiv:2609.33566 · cs.LG, stat.ML · Submitted 2026-09-27 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-09-27

Updated: 2026-09-27

Code: https://github.com/decisionintelligence/TFB

Terminology

• Time Series Forecasting — 24× in this paper · explained in 3 episodes
• Consistency — 20× in this paper · explained in 3 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• State-Space Model — 8× in this paper
• Transformer — 7× in this paper · explained in 2 episodes
• Latent Dynamics — 6× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Chaotic Dynamics — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Dynamic Modeling — 1× in this paper · explained in 3 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Hilbert Space — 1× in this paper · explained in 2 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Probabilistic Forecasting — 1× in this paper · explained in 3 episodes
• System Identification — 1× in this paper · explained in 3 episodes

Sources

• Adversarial Robustness of Deep State Space Models for Forecasting
• Neural Machine Translation by Jointly Learning to Align and Translate
• Teacher Forcing as Generalized Bayes: Optimization Geometry Mismatch in Switching Surrogates for Chaotic Dynamics
• Learning-based Design of Luenberger Observers for Autonomous Nonlinear Systems

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