论文标题
Shape-CD：具有形状和神经元的时间序列数据中的更改点检测
Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons
论文作者
论文摘要
时间序列中的更改点检测旨在发现某些生成时间序列数据的未知基础物理过程的时间点已更改。我们发现，当基础过程复杂并在时间序列中产生大量模式时，现有方法变得不准确。为了解决这一缺点，我们提出了一种简单，快速，准确的更改点检测方法。 Shape-CD使用基于形状的特征来对模式和条件神经场进行建模，以建模时间区域之间的时间相关性。我们使用四个高度动态的时间序列数据集评估了Shape-CD的性能，其中包括具有多达2000个类的外感应数据集。与现有方法相比，Shape-CD表现出提高的精度（AUC中高7-60％）和更快的计算速度。此外，与其他深入监督的学习模型相比，Shape-CD模型仅由数百个参数组成，并且需要更少的数据来训练。
Change-point detection in a time series aims to discover the time points at which some unknown underlying physical process that generates the time-series data has changed. We found that existing approaches become less accurate when the underlying process is complex and generates large varieties of patterns in the time series. To address this shortcoming, we propose Shape-CD, a simple, fast, and accurate change point detection method. Shape-CD uses shape-based features to model the patterns and a conditional neural field to model the temporal correlations among the time regions. We evaluated the performance of Shape-CD using four highly dynamic time-series datasets, including the ExtraSensory dataset with up to 2000 classes. Shape-CD demonstrated improved accuracy (7-60% higher in AUC) and faster computational speed compared to existing approaches. Furthermore, the Shape-CD model consists of only hundreds of parameters and require less data to train than other deep supervised learning models.
