基于改进Transformer的剩余时间预测
Remaining Time Prediction Based on Improved Transformer
刘海洲 1高俊涛1
作者信息
- 1. 东北石油大学计算机与信息技术学院,大庆 163318
- 折叠
摘要
剩余时间预测能够帮助企业提升业务流程执行的质量和效率.尽管现有的深度学习方法在剩余时间预测上有一定提升,但在处理复杂业务流程时,仍面临时间特征利用不足和局部特征挖掘能力有限的问题,预测精度有待提高.为此,本研究提出了一种基于改进Transformer编码器模型的剩余时间预测方法.针对已有方法忽略事件时间特征以及难以捕捉局部依赖的不足,本研究在模型中引入了时间特征编码模块和局部依赖增强模块.时间编码模块通过嵌入学习和多粒度拼接方式,构建了富有语义且具判别力的事件时间表示.局部依赖增强模块采用卷积神经网络,在Transformer编码器之后提取轨迹前缀的局部细节特征.实验表明,融合时间特征和局部依赖增强可以提升复杂业务流程剩余时间的预测准确性.
Abstract
Remaining time prediction helps enterprises improve the quality and efficiency of business process execution.Although existing deep learning methods have shown improvement in remaining time prediction,they still face challenges when dealing with complex business processes.These challenges include insufficient utilization of time features and limited ability to extract local features,leaving room for improvement in prediction accuracy.This study proposes a remaining time prediction method based on the improved Transformer encoder model.Existing methods ignore event time features and struggle to capture local dependencies.To address these limitations,this study introduces a time feature encoding module and a local dependency enhancement module into the model.The time encoding module constructs a semantically rich and discriminative event time representation by embedding learning and multi-granularity concatenation.The local dependency enhancement module uses convolutional neural networks to extract fine-grained local features from the trajectory prefix after processing with the Transformer encoder.Experiments show that integrating time features and local dependency enhancement improves the prediction accuracy of the remaining time for complex business processes.
关键词
剩余时间预测/过程挖掘/深度学习/TransformerKey words
remaining time prediction/process mining/deep learning/Transformer引用本文复制引用
出版年
2024
NETL
NSTL
万方数据