Most time series foundation models are pretrained by directly predicting future observations, which often yields weakly structured latent representations that capture surface noise rather than coherent and predictable temporal dynamics. In this work, we introduce EIDOS, a foundation model family that shifts pretraining from future value prediction to latent-space predictive learning. We train a causal Transformer to predict the evolution of latent representations, encouraging the emergence of structured and temporally coherent latent states. To ensure stable targets for latent-space learning, we design a lightweight aggregation branch to construct target representations. EIDOS is optimized via a joint objective that integrates latent-space alignment, observational grounding to anchor representations to the input signal, and direct forecasting supervision. On the GIFT-Eval benchmark, EIDOS mitigates structural fragmentation in the representation space and achieves state-of-the-art performance. These results demonstrate that constraining models to learn predictable latent dynamics is a principled step toward more robust and reliable time series foundation models.

翻译：大多数时间序列基础模型通过直接预测未来观测值进行预训练，这通常会产生弱结构化的隐表示，其捕捉的是表层噪声而非连贯且可预测的时序动态。本工作中，我们提出了EIDOS——一个将预训练从未来值预测转向隐空间预测学习的基础模型系列。我们训练一个因果Transformer来预测隐表示的演化过程，从而促进结构化且时序连贯的隐状态的形成。为确保隐空间学习具有稳定的目标，我们设计了一个轻量级聚合分支来构建目标表示。EIDOS通过联合目标函数进行优化，该函数整合了隐空间对齐、将表示锚定至输入信号的观测基础化以及直接预测监督。在GIFT-Eval基准测试中，EIDOS缓解了表示空间中的结构碎片化问题，并取得了最先进的性能。这些结果表明，约束模型以学习可预测的隐动态是迈向更鲁棒、更可靠的时间序列基础模型的关键一步。