Unsupervised representation learning approaches aim to learn discriminative
feature representations from unlabeled data, without the requirement of
annotating every sample. Enabling unsupervised representation learning is
extremely crucial for time series data, due to its unique annotation bottleneck
caused by its complex characteristics and lack of visual cues compared with
other data modalities. In recent years, unsupervised representation learning
techniques have advanced rapidly in various domains. However, there is a lack
of systematic analysis of unsupervised representation learning approaches for
time series. To fill the gap, we conduct a comprehensive literature review of
existing rapidly evolving unsupervised representation learning approaches for
time series. Moreover, we also develop a unified and standardized library,
named ULTS (i.e., Unsupervised Learning for Time Series), to facilitate fast
implementations and unified evaluations on various models. With ULTS, we
empirically evaluate state-of-the-art approaches, especially the rapidly
evolving contrastive learning methods, on 9 diverse real-world datasets. We
further discuss practical considerations as well as open research challenges on
unsupervised representation learning for time series to facilitate future
research in this field.

无监督时间序列数据表示学习方法的文献综述及开放性研究挑战的综合分析，通过统一标准化库对其进行了实证评估，并讨论了实际考虑因素和未来研究方向。

时间序列的无监督表示学习综述

Unsupervised Representation Learning for Time Series: A Review

With the upsurge of interest in artificial intelligence machine learning (ML)
algorithms, originally developed in academic environments, are now being
deployed as parts of real-life systems that deal with large amounts of
heterogeneous, dynamic, and high-dimensional data. Deployment of ML methods in
real life is prone to challenges across the whole system life-cycle from data
management to systems deployment, monitoring, and maintenance. Data-Oriented
Architecture (DOA) is an emerging software engineering paradigm that has the
potential to mitigate these challenges by proposing a set of principles to
create data-driven, loosely coupled, decentralised, and open systems. However
DOA as a concept is not widespread yet, and there is no common understanding of
how it can be realised in practice. This review addresses that problem by
contextualising the principles that underpin the DOA paradigm through the ML
system challenges. We explore the extent to which current architectures of
ML-based real-world systems have implemented the DOA principles. We also
formulate open research challenges and directions for further development of
the DOA paradigm.

本文主要讨论了在现实生活中，应用机器学习的算法所面临的各种挑战，提出了数据导向架构（DOA）作为一种解决方案，探讨了现有的基于机器学习的实际系统是否实现了 DOA 的原则，并提出了 DOA 进一步发展的研究方向和挑战。

以数据为导向架构视角的实际机器学习系统调查

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

The dynamicity of real-world systems poses a significant challenge to
deployed predictive machine learning (ML) models. Changes in the system on
which the ML model has been trained may lead to performance degradation during
the system's life cycle. Recent advances that study non-stationary environments
have mainly focused on identifying and addressing such changes caused by a
phenomenon called concept drift. Different terms have been used in the
literature to refer to the same type of concept drift and the same term for
various types. This lack of unified terminology is set out to create confusion
on distinguishing between different concept drift variants. In this paper, we
start by grouping concept drift types by their mathematical definitions and
survey the different terms used in the literature to build a consolidated
taxonomy of the field. We also review and classify performance-based concept
drift detection methods proposed in the last decade. These methods utilize the
predictive model's performance degradation to signal substantial changes in the
systems. The classification is outlined in a hierarchical diagram to provide an
orderly navigation between the methods. We present a comprehensive analysis of
the main attributes and strategies for tracking and evaluating the model's
performance in the predictive system. The paper concludes by discussing open
research challenges and possible research directions.

本文介绍了预测机器学习模型在实际应用中由于系统动态性带来的性能退化问题，对概念漂移的类型做出了统一分类，并对最近十年中提出的基于性能的概念漂移检测方法进行了回顾和分类，还提出了跟踪和评估预测系统中模型性能的主要属性和策略，并探讨了开放性研究挑战和可能的研究方向。