计算机应用与软件2025，Vol.42Issue(4)：295-302,8.DOI:10.3969/j.issn.1000-386x.2025.04.042
面向时间序列相似性检测的深度哈希网络
DEEP HASH NETWORK FOR TIME SERIES SIMILARITY DETECTION
摘要
Abstract
Time series similarity detection plays a critical role in scenarios such as financial data analysis and power data mining.To address the quantization loss issue in existing deep hashing networks for time series,we propose an end-to-end Deep Contrastive Time Series Hash(DCTSH)network.By introducing an adaptive binarization network and hash loss,the method eliminates quantization errors during binary hashing,enabling the model to generate time series hash codes with enhanced expressive effectiveness and generalization capability through end-to-end training.For unlabeled time series data,the negative sample selection in the contrastive learning network is improved via clustering to strengthen time series representation learning.Experimental results on multiple time series datasets demonstrate that DCTSH achieves significantly improved detection accuracy compared to previous methods.关键词
深度哈希/相似检测/时间序列/对比学习Key words
Deep Hash/Similarity detection/Time series/Deep learning/Contrastive learning分类
计算机与自动化引用本文复制引用
李轩,徐旻洋,周向东..面向时间序列相似性检测的深度哈希网络[J].计算机应用与软件,2025,42(4):295-302,8.基金项目
国家重点研发计划项目(2018YFB1402600). （2018YFB1402600）
