The goal of the change-point detection is to discover changes of time series distribution. One of the state of the art approaches of the change-point detection are based on direct density ratio estimation. In this work we show how existing algorithms can be generalized using various binary classification and regression models. In particular, we show that the Gradient Boosting over Decision Trees and Neural Networks can be used for this purpose. The algorithms are tested on several synthetic and real-world datasets. The results show that the proposed methods outperform classical RuLSIF algorithm. Discussion of cases where the proposed algorithms have advantages over existing methods are also provided.
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Gina Cody School of Engineering and Computer Science（吉娜·科迪工程与计算机科学学院）；Concordia University（康考迪亚大学）；CHU Sainte-Justine Research Center（圣贾斯汀大学医院研究中心）
Out-of-Distribution Generalization in Time Series: A Survey
School of Computing and Artificial Intelligence, Southwest Jiaotong University（计算机与人工智能学院，西南交通大学）；Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education（可持续城市智能交通工程研究中心，教育部）；Information Sciences and Technology Department, the Pennsylvania State University（信息科学与技术系，宾夕法尼亚州立大学）
Locally Linear Continual Learning for Time Series based on VC-Theoretical Generalization Bounds
Department of Electronic Engineering, Universidade Federal de Minas Gerais（米纳斯格拉斯联邦大学电子工程系）；Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais（米纳斯格拉斯联邦大学电气工程研究生项目）
Theoretical Foundation of Flow-Based Time Series Generation: Provable Approximation, Generalization, and Efficiency
South China University of Technology（华南理工大学）；The Simons Institute for the Theory of Computing at the UC, Berkeley（加州大学伯克利分校西蒙斯理论计算研究所）；Sun Yat-sen University（中山大学）
本文针对基于流的时间序列生成缺乏理论解释的问题，建立涵盖逼近、泛化与效率的理论框架，证明 DiT 任意误差拟合、多项式正则化泛化界及采样快速收敛。
BioFormer: Rethinking Cross-Subject Generalization via Spectral Structural Alignment in Biomedical Time-Series
College of Computer Science, Nankai University, Tianjin, China（南开大学计算机科学学院）；College of Cyber Science, Tianjin Key Laboratory of Interventional Brain-Computer Interface（天津介入脑机接口与智能康复重点实验室）；Intelligent Rehabilitation, Key Lab of Data（智能康复，数据实验室）；Intelligent System Security, Frontiers Science Center for New Organic Matter, Nankai University, Tianjin, China（智能系统安全，新有机物前沿科学中心，南开大学，天津，中国）
The language of time: a language model perspective on time-series foundation models
College of Computer Science and Artificial Intelligence, Fudan University（计算机科学与人工智能学院，复旦大学）；Shanghai Key Laboratory of Data Science（上海数据科学重点实验室）；ZCTech；School of Mathematics and Statistics, Beijing Institute of Technology（数学与统计学学院，北京理工大学）
