The staggering amount of streaming time series coming from the real world calls for more efficient and effective online modeling solution. For time series modeling, most existing works make some unrealistic assumptions such as the input data is of fixed length or well aligned, which requires extra effort on segmentation or normalization of the raw streaming data. Although some literature claim their approaches to be invariant to data length and misalignment, they are too time-consuming to model a streaming time series in an online manner. We propose a novel and more practical online modeling and classification scheme, DDE-MGM, which does not make any assumptions on the time series while maintaining high efficiency and state-of-the-art performance. The derivative delay embedding (DDE) is developed to incrementally transform time series to the embedding space, where the intrinsic characteristics of data is preserved as recursive patterns regardless of the stream length and misalignment. Then, a non-parametric Markov geographic model (MGM) is proposed to both model and classify the pattern in an online manner. Experimental results demonstrate the effectiveness and superior classification accuracy of the proposed DDE-MGM in an online setting as compared to the state-of-the-art.
MTD: Multi-Timestep Detector for Delayed Streaming Perception
School of Computer and Electronic Information, Guangxi University（广西大学计算机与电子信息学院）；Guangxi Intelligent Digital Services Research Center of Engineering Technology（广西智能数字服务工程技术研究中心）
Evaluating the Efficacy of Instance Incremental vs. Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection
Sorbonne Paris Nord University（巴黎北大学）；Paris-Saclay University（巴黎萨克雷大学）；Groupe BPCE（BPCE集团）
A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
organization= Department of Mechanical Engineering, University of Utah , addressline= , city= Salt Lake City , postcode= 84112 , state= UT , country= USA；organization= Kahlert School of Computing, University of Utah , city= Salt Lake City , postcode= 84112 , state= UT , country= USA；organization= Scientific Computing \& Imaging Institute, University of Utah , addressline= , city= Salt Lake City , postcode= 84112 , state= UT , country= USA
Weakly Supervised Deep Learning Approach in Streaming Environments
Nanyang Technological University（南洋理工大学）
现有的数据流算法的可行性往往受到数据流的弱监督条件的阻碍。提出了一种自进化的深度神经网络，即Parsimonious Network（ParsNet），用于解决各种弱监督数据流问题。提出了一种带套期保值的自标记策略（SLASH），它的自校正机制处理了影响模型泛化能力的\textit{the accumulation of mistakes}。ParsNet是从自进化生成和判别训练过程的闭环配置开发的，利用共享参数，其结构灵活地增长和收缩，以克服有/无标签的概念漂移问题。数值评估已经进行了两个具有挑战性的问题，即零星访问地面真理和无限延迟访问地面真理。我们的数值研究显示了ParsNet的优势，在高维数据流和无限延迟模拟协议方面与同行有很大的差距。为了支持可重复的研究计划，ParsNet的源代码以及补充材料可在https://bit.ly/2qNW7p4上获得。
TSGuard: A Real-Time Framework for Detecting and Imputing Missing Data in Streaming Time Series
University of Luxembourg（卢森堡大学）；Hassiba Benbouali University of Chlef（谢莱夫哈西巴·本布阿里大学）；Université Paris Cité（巴黎西岱大学）；École Supérieure d’Informatique（高等计算机学院）
