From Observations to States: Latent Time Series Forecasting

2026-01-30

AI Summary

This work addresses a critical yet previously unarticulated issue in time series forecasting—termed “latent chaos”—where conventional methods operating directly in the observation space learn representations that are temporally inconsistent and lack continuity, thereby failing to capture the true underlying dynamics of the system. To overcome this limitation, the paper introduces LatentTSF, a novel paradigm that leverages an autoencoder to construct a high-dimensional latent state space in which prediction is performed. By implicitly maximizing the mutual information among latent states, ground-truth system states, and observations, LatentTSF enforces temporal coherence and dynamical fidelity. Theoretical analysis and extensive experiments demonstrate that this approach substantially mitigates latent chaos and achieves state-of-the-art forecasting performance across multiple established benchmarks.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 phenomenon to the dominant observation-space forecasting paradigm. Most TSF models minimize point-wise errors on noisy and partially observed data, which encourages shortcut solutions instead of the recovery of underlying system dynamics. To address this issue, we propose Latent Time Series Forecasting (LatentTSF), a novel paradigm that shifts TSF from observation regression to latent state prediction. Specifically, LatentTSF employs an AutoEncoder to project observations at each time step into a higher-dimensional latent state space. This expanded representation aims to capture underlying system variables and impose a smoother temporal structure. Forecasting is then performed entirely in the latent space, allowing the model to focus on learning structured temporal dynamics. Theoretical analysis demonstrates that our proposed latent objectives implicitly maximize mutual information between predicted latent states and ground-truth states and observations. Extensive experiments on widely-used benchmarks confirm that LatentTSF effectively mitigates latent chaos, achieving superior performance. Our code is available in https://github.com/Muyiiiii/LatentTSF.

Problem

• Latent Chaos
• Time Series Forecasting
• Latent Representation
• Observation-space Forecasting
• System Dynamics

Innovation

• Latent Time Series Forecasting
• Latent Chaos
• AutoEncoder
• Latent State Prediction
• Mutual Information