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
station- ary 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

When and How: Learning Identifiable Latent States for Nonstationary Time  Series Forecasting

In this work, we propose a hyperparameter optimization method named
\emph{HyperTime} to find hyperparameters robust to potential temporal
distribution shifts in the unseen test data. Our work is motivated by an
important observation that it is, in many cases, possible to achieve temporally
robust predictive performance via hyperparameter optimization. Based on this
observation, we leverage the `worst-case-oriented' philosophy from the robust
optimization literature to help find such robust hyperparameter configurations.
HyperTime imposes a lexicographic priority order on average validation loss and
worst-case validation loss over chronological validation sets. We perform a
theoretical analysis on the upper bound of the expected test loss, which
reveals the unique advantages of our approach. We also demonstrate the strong
empirical performance of the proposed method on multiple machine learning tasks
with temporal distribution shifts.

本研究提出了一种名为 HyperTime 的超参数优化方法，通过基于最劣情况的优化理念，结合对时间分布转移的鲁棒性考虑，优化机器学习模型的配置，提高对未来数据的预测能力。