摘要
时间序列相似检测在金融数据、电力数据挖掘等场景都有很重要的作用。为了解决时间序列深度哈希网络存在哈希量化损失的问题,提出一种端到端的深度对比学习时间序列哈希网络(Deep Contrastive Time Series Hash,DCTSH)。通过引入自适应二值化网络与哈希损失,消除二值化哈希时的量化误差,使得模型端到端训练生成的时间序列哈希编码,具有更好的表达效果与泛化能力。针对无标签时间序列数据,通过聚类改进对比学习网络的负样本选择来增强时间序列表示学习能力。在多个时间序列数据集上实验结果表明,DCTSH相较于之前的方法检测精度显著提升。
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.
作者
李轩
徐旻洋
周向东
Li Xuan;Xu Minyang;Zhou Xiangdong(School of Computer Science,Fudan University,Shanghai 200433,China;Arcplus Group PLC,Shanghai 200041,China)
出处
《计算机应用与软件》
北大核心
2025年第4期295-302,共8页
Computer Applications and Software
基金
国家重点研发计划项目(2018YFB1402600)。
关键词
深度哈希
相似检测
时间序列
对比学习
Deep Hash
Similarity detection
Time series
Deep learning
Contrastive learning
