提出新框架，让模型预测隐藏状态而非直接预测观测值。
From Observations to States: Latent Time Series Forecasting
- 用自编码器将观测转为隐状态，全程在隐空间预测
- 在多个基准上提升准确率与表示质量，解决隐状态混乱问题
- 适合关注时序建模本质、追求可解释性的研究者
深度学习在时间序列预测（TSF）中表现优异，但我们发现一个关键的表征悖论：隐状态混沌——预测准确的模型往往学习到时间上无序、缺乏连续性的隐表示。这源于主流的观测空间预测范式：在噪声大且部分可观测的数据上最小化逐点误差，会诱导出捷径解，而非恢复底层系统动态。为此，我们提出隐状态时间序列预测（LatentTSF），将TSF从观测回归转向隐状态预测。该方法使用自编码器将每个观测投影至学习得到的隐状态空间，并完全在此空间内进行预测，使模型能聚焦于学习结构化的时序动态。我们提供信息论分析，表明隐空间目标可视为最大化预测隐状态与真实隐状态及未来观测之间互信息的代理。大量实验证明，LatentTSF有效缓解了隐状态混沌，在多个常用基准上持续提升预测精度与表示质量。代码已开源。
原文摘要 · Abstract (English)
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.
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