This paper addresses the problem of detecting time series outliers, focusing
on systems with repetitive behavior, such as industrial robots operating on
production lines.Notable challenges arise from the fact that a task performed
multiple times may exhibit different duration in each repetition and that the
time series reported by the sensors are irregularly sampled because of data
gaps. The anomaly detection approach presented in this paper consists of three
stages.The first stage identifies the repetitive cycles in the lengthy time
series and segments them into individual time series corresponding to one task
cycle, while accounting for possible temporal distortions.The second stage
computes a prototype for the cycles using a GPU-based barycenter algorithm,
specifically tailored for very large time series.The third stage uses the
prototype to detect abnormal cycles by computing an anomaly score for each
cycle.The overall approach, named WarpEd Time Series ANomaly Detection
(WETSAND), makes use of the Dynamic Time Warping algorithm and its variants
because they are suited to the distorted nature of the time series.The
experiments show that \wetsand scales to large signals, computes human-friendly
prototypes, works with very little data, and outperforms some general purpose
anomaly detection approaches such as autoencoders.

该研究论文探讨了检测时间序列异常值的问题，重点关注具有重复行为的系统，例如在生产线上操作的工业机器人。通过使用特定于大型时间序列的基于 GPU 的质心算法，该论文提出了一种异常检测方法，该方法可以识别出重复周期并计算异常得分，以检测异常循环。实验证明，该方法能够处理大型信号，计算人类友好的原型，使用非常少的数据，并且优于一些通用的异常检测方法，如自编码器。

扭曲时间序列异常检测

Warped Time Series Anomaly Detection

Classifying videos into distinct categories, such as Sport and Music Video,
is crucial for multimedia understanding and retrieval, especially in an age
where an immense volume of video content is constantly being generated.
Traditional methods require video decompression to extract pixel-level features
like color, texture, and motion, thereby increasing computational and storage
demands. Moreover, these methods often suffer from performance degradation in
low-quality videos. We present a novel approach that examines only the
post-compression bitstream of a video to perform classification, eliminating
the need for bitstream. We validate our approach using a custom-built data set
comprising over 29,000 YouTube video clips, totaling 6,000 hours and spanning
11 distinct categories. Our preliminary evaluations indicate precision,
accuracy, and recall rates well over 80%. The algorithm operates approximately
15,000 times faster than real-time for 30fps videos, outperforming traditional
Dynamic Time Warping (DTW) algorithm by six orders of magnitude.

视频分类中，通过研究视频的压缩比特流来代替传统方法中涉及视频解压缩的特征提取，以提高分类性能和处理速度。通过验证自定义数据集，结果表明该方法在精度、准确率和召回率方面都超过 80%，而且处理速度是真实时间的 15000 倍，比传统的动态时间规整算法效果好六个数量级。