提出深度距离度量方法，精准捕捉工业时序数据中的微小差异。
Deep Distance Measurement Method for Unsupervised Multivariate Time Series Similarity Retrieval
- 基于成对样本加权学习，自动聚焦时序中细微状态差异。
- 在造纸厂数据集上显著超越现有方法，提升检索准确率。
- 可与现有特征提取方法结合，适用于工业异常检测场景。
我们提出深度距离度量方法（DDMM），以提升无监督多变量时间序列相似性检索的准确性。DDMM能够学习整个时间序列中状态间的微小差异，并识别用户关注的细微状态变化。为此，该方法设计一种学习算法，根据锚点与正样本对之间的欧氏距离为每对样本分配权重，并基于权重学习样本对内的差异。该机制既实现了对状态内微小差异的学习，又允许从整个时间序列中任意采样样本对。实证研究表明，DDMM在造纸厂数据集上显著优于当前最优的时间序列表示学习方法，验证了其在工业场景中的有效性。此外，通过实验表明，将DDMM与现有特征提取方法结合后，可进一步提升检索精度。
原文摘要 · Abstract (English)
We propose the Deep Distance Measurement Method (DDMM) to improve retrieval accuracy in unsupervised multivariate time series similarity retrieval. DDMM enables learning of minute differences within states in the entire time series and thereby recognition of minute differences between states, which are of interest to users in industrial plants. To achieve this, DDMM uses a learning algorithm that assigns a weight to each pair of an anchor and a positive sample, arbitrarily sampled from the entire time series, based on the Euclidean distance within the pair and learns the differences within the pairs weighted by the weights. This algorithm allows both learning minute differences within states and sampling pairs from the entire time series. Our empirical studies showed that DDMM significantly outperformed state-of-the-art time series representation learning methods on the Pulp-and-paper mill dataset and demonstrated the effectiveness of DDMM in industrial plants. Furthermore, we showed that accuracy can be further improved by linking DDMM with existing feature extraction methods through experiments with the combined model.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。