Temporal distribution shifts are ubiquitous in time series data. One of the most popular methods assumes that the temporal distribution shift occurs uniformly to disentangle the stationary and nonstationary dependencies. But this assumption is difficult to meet, as we do not know when the distribution shifts occur. To solve this problem, we propose to learn IDentifiable latEnt stAtes (IDEA) to detect when the distribution shifts occur. Beyond that, we further disentangle the stationary and nonstationary latent states via sufficient observation assumption to learn how the latent states change. Specifically, we formalize the causal process with environment-irrelated stationary and environment-related nonstationary variables. Under mild conditions, we show that latent environments and stationary/nonstationary variables are identifiable. Based on these theories, we devise the IDEA model, which incorporates an autoregressive hidden Markov model to estimate latent environments and modular prior networks to identify latent states. The IDEA model outperforms several latest nonstationary forecasting methods on various benchmark datasets, highlighting its advantages in real-world scenarios.

我们提出了一种基于可识别的潜在环境状态（IDEA）的方法来检测分布转变发生的时间，并通过充分观察假设来解耦稳态和非稳态潜在状态的变化，其中包括自回归隐马尔可夫模型来估计潜在环境和模块化先验网络来识别潜在状态。在各种基准数据集上，IDEA模型优于几种最新的非稳态预测方法，突显了其在实际场景中的优势。

何时和如何：学习可识别的非固定时间序列预测潜在状态