Identifiability of latent variable models has recently gained interest in
terms of its applications to interpretability or out of distribution
generalisation. In this work, we study identifiability of Markov Switching
Models as a first step towards extending recent results to sequential latent
variable models. We present identifiability conditions within first-order
Markov dependency structures, and parametrise the transition distribution via
non-linear Gaussians. Our experiments showcase the applicability of our
approach for regime-dependent causal discovery and high-dimensional time series
segmentation.

本文研究了 Markov Switching Models 的可识别性，探讨了其在序列潜变量模型中的应用，并给出了基于非线性高斯的转移分布参数化方法，实验结果表明此方法可用于因果发现和高维时间序列分割。

马尔科夫转换模型的可辨识性

On the Identifiability of Markov Switching Models

The study of natural and human-made processes often results in long sequences
of temporally-ordered values, aka time series (TS). Such processes often
consist of multiple states, e.g. operating modes of a machine, such that state
changes in the observed processes result in changes in the distribution of
shape of the measured values. Time series segmentation (TSS) tries to find such
changes in TS post-hoc to deduce changes in the data-generating process. TSS is
typically approached as an unsupervised learning problem aiming at the
identification of segments distinguishable by some statistical property.
Current algorithms for TSS require domain-dependent hyper-parameters to be set
by the user, make assumptions about the TS value distribution or the types of
detectable changes which limits their applicability. Common hyperparameters are
the measure of segment homogeneity and the number of change points, which are
particularly hard to tune for each data set. We present ClaSP, a novel, highly
accurate, hyper-parameter-free and domain-agnostic method for TSS. ClaSP
hierarchically splits a TS into two parts. A change point is determined by
training a binary TS classifier for each possible split point and selecting the
one split that is best at identifying subsequences to be from either of the
partitions. ClaSP learns its main two model-parameters from the data using two
novel bespoke algorithms. In our experimental evaluation using a benchmark of
107 data sets, we show that ClaSP outperforms the state of the art in terms of
accuracy and is fast and scalable. Furthermore, we highlight properties of
ClaSP using several real-world case studies.

该论文介绍了 ClaSP 一种基于二元时间序列分类器的无超参数时间序列分割方法，通过两种新方法学习主要参数，并在 107 个数据集基准测试中优于现有技术，无需设置超参数且快速可扩展。