基于图持续学习的时序数据分析

Time Series Data Analysis Based on Graph Continuous Learning

随着可穿戴设备大规模进入生活,基于动作传感器产生的时序数据来人体行为识别已成为该领域的研究热点.然而目前的方法无法发现多个传感器数据在时空中相互作用的关系.此外,传统神经网络在学习新任务时,由于学习的新任务参数会覆盖掉旧任务参数,这会引起“灾难性遗忘”问题.为解决这两个问题,本文提出了一种基于图注意力网络与生成式回放持续学习机制融合方法的人体行为识别算法.该算法通过卷积神经网络与图注意力网络提取时序特征,使得模型能够同时关注时间与空间特征,同时,采用了基于生成式数据重放策略的情景记忆持续学习方法,通过条件变分自编码器记忆历史数据分布来解决灾难性遗忘问题.最后,通过在多个公开数据集上与不同的基线算法对比,实验结果表明本文所提算法可以在取得较高的准确率的同时,缓解灾难性遗忘问题.

With wearable devices entering life on a large scale,human behavior recognition based on temporal data generated by motion sensors has become a research hotspot in this field.However,the current methods cannot find the relationship between multiple sensor data in time and space.In addition,when the traditional neural network learns a new task,the new task parameters will overwrite the old task parameters,causing catastrophic forgetting problems.To this end,this study proposes a human behavior recognition algorithm based on the fusion method of graph attention network and generative playback continuous learning mechanism.The algorithm extracts temporal features through convolutional neural network and graph attention network,enabling the model to focus on temporal and spatial features at the same time.In addition,the algorithm adopts an episodic memory continuous learning method based on a generative data replay strategy,which remembers historical data distributions by conditional variational autoencoders,to address the catastrophic forgetting problem.Finally,compared with different baseline algorithms on multiple public datasets,the experimental results show that the proposed algorithm can achieve higher accuracy while mitigating the catastrophic forgetting problem more effectively.