From Observations to States: Latent Time Series Forecasting

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 https://github.com/Muyiiiii/LatentTSF.

Predicting the future from past data, such as forecasting electricity demand, traffic flow, or stock prices, is one of the most important uses of AI. Modern AI models achieve impressive accuracy on these tasks. However, we discovered a hidden paradox: these accurate models don't truly understand the underlying patterns in time. When we examined what they had "learned" internally, we found their understanding was disorganized, like a student who can recite correct answers without truly grasping the material. We call this phenomenon "Latent Chaos." We traced the problem to how these models are trained: they only care about matching surface-level numbers, not about grasping the dynamics behind them. To fix this, we developed LatentTSF, a new training approach. Instead of predicting raw observations directly, we first translate the data into a cleaner internal representation of the system's state, then have the model reason within that structured space. Across six real-world datasets covering electricity, traffic, and weather, our approach improves prediction accuracy and produces models that better understand time, remaining reliable even when input data is noisy or incomplete. This is a step toward AI that truly comprehends temporal patterns, rather than just matching patterns.