Abstract:Deep learning has achieved strong performance in Time Series Forecasting (TSF). However, we identify a critical representation paradox, termed Latent Chaos: models with accurate predictions often learn latent representations that are temporally disordered and lack continuity. We attribute this to the dominant observation-space forecasting paradigm, where minimizing point-wise errors on noisy and partially observed data encourages shortcut solutions instead of the recovery of underlying system dynamics. To address this, we propose Latent Time Series Forecasting (LatentTSF), a paradigm that shifts TSF from observation regression to latent state prediction. LatentTSF employs an AutoEncoder to project each observation into a learned latent state space and performs forecasting entirely in this space, allowing the model to focus on learning structured temporal dynamics. We provide an information-theoretic analysis showing that the latent objectives can be motivated as surrogates for maximizing mutual information between predicted and ground-truth latent states and future observations. Extensive experiments on widely-used benchmarks confirm that LatentTSF effectively mitigates latent chaos, yielding consistent improvements in both forecasting accuracy and representation quality. Our code is available at this https URL.
| Comments: | Accepted at ICML 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2602.00297 [cs.LG] |
| (or arXiv:2602.00297v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.00297 arXiv-issued DOI via DataCite |
From: Jie Yang [view email]
[v1]
Fri, 30 Jan 2026 20:39:44 UTC (11,197 KB)
[v2]
Tue, 12 May 2026 15:25:15 UTC (11,202 KB)
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