Multidimensional time series are sequences of real valued vectors. They occur in different areas, for example handwritten characters, GPS tracking, and gestures of modern virtual reality motion controllers. Within these areas, a common task is to search for similar time series. Dynamic Time Warping (DTW) is a common distance function to compare two time series. The Edit Distance with Real Penalty (ERP) and the Dog Keeper Distance (DK) are two more distance functions on time series. Their behaviour has been analyzed on 1-dimensional time series. However, it is not easy to evaluate their behaviour in relation to growing dimensionality. For this reason we propose two new data synthesizers generating multidimensional time series. The first synthesizer extends the well known cylinder-bell-funnel (CBF) dataset to multidimensional time series. Here, each time series has an arbitrary type (cylinder, bell, or funnel) in each dimension, thus for $d$-dimensional time series there are $3^{d}$ different classes. The second synthesizer (RAM) creates time series with ideas adapted from Brownian motions which is a common model of movement in physics. Finally, we evaluate the applicability of a 1-nearest neighbor classifier using DTW on datasets generated by our synthesizers.
Stochastic generator of trajectories from record data: application to the fluctuations of a glacier's frontal position from a sample of moraines
Laboratoire des Sciences du Climat et de l'Environnement（气候与环境科学实验室）；Mines Paris PSL（巴黎矿业学院PSL）；CEREGE, Centre de Recherche et d'Enseignement en Géosciences de l'Environnement（环境地球科学研究与教学中心）
Detecting Diffusion-Generated Time Series Under Generator Shift
TU Dortmund University\ of Neuch\ a tel Dortmund Germany；Delft University of Technology Delft Netherlands；University of Neuch\ a tel Neuch\ a tel Switzerland；TU Dortmund University Dortmund Germany；RWTH Aachen University\ Dortmund University Aachen Germany；University of Neuch\ a tel\ University of Technology Neuch\ a tel Switzerland；TU Dortmund University\ of Neuch\ a tel；Delft University of Technology；University of Neuch\ a tel；TU Dortmund University；RWTH Aachen University\ Dortmund University；University of Neuch\ a tel\ University of Technology
GenIAS: Generator for Instantiating Anomalies in time Series
Monash University, Melbourne, Victoria, Australia（墨尔本大学）；IBM T. J. Watson Research Center, Yorktown Heights, NY, USA（IBM 沃森研究中心）；Griffith University, Gold Coast, Queensland, Australia（格里菲斯大学）；The University of Melbourne, Parkville, Victoria, Australia（墨尔本大学）
Are Good Generators Good Decision-Makers? Policy Learning for General Interventions via Retargeted Counterfactual Generation
Cornell Tech（康奈尔科技）；Cornell University（康奈尔大学）；Department of Biostatistics, Department of Population Health（生物统计学系、人口健康系）；New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）
Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting
University of Minho（米尼奥大学）；Universidade do Porto（波尔图大学）；Laboratory for Artificial Intelligence and Computer Science (LIACC)（人工智能与计算机科学实验室（LIACC））；Fraunhofer Portugal AICOS（弗劳恩霍夫葡萄牙AICOS机构）；INESCTEC；University of Coimbra（科英布拉大学）
Generative clinical time series models trained on moderate amounts of patient data are privacy preserving
Mathematical Modelling and Data Analysis Department, Physikalisch-Technische Bundesanstalt (PTB)（物理技术联邦机构（PTB）数学建模与数据分析部门）；Institute of Medical Informatics, Charité – Universitätsmedizin Berlin（柏林查理医院医学信息学研究所）
