计算机科学 ›› 2024, Vol. 51 ›› Issue (3): 128-134.doi: 10.11896/jsjkx.221200055
郑伟楠1, 於志勇1,2, 黄昉菀1,2
ZHENG Weinan1, YU Zhiyong1,2, HUANG Fangwan1,2
摘要： 随着物联网的发展,众多传感器采集到大量具有丰富数据相关性的时间序列,为各种数据挖掘应用提供强大的数据支持。然而,一些客观或主观原因(如设备故障、稀疏感知等)往往会造成采集到的数据出现不同程度的缺失。虽然已有很多方法被提出用于解决这一问题,但这些方法在数据相关性方面或考虑不够全面,或计算成本过高。而且,现有方法仅关注对缺失值的补全,未能兼顾下游应用。针对上述不足,设计了一种兼顾补全与预测任务的双通道回声状态网络。两个通道的网络虽共用输入层,但具有各自的储备池和输出层。两者最大的区别是左/右通道的输出层分别表示输入层前/后一个时刻对应的目标值或预补值。最后将两个通道的估计值进行融合,充分利用来自缺失时刻之前和之后的数据相关性以进一步提升性能。两种缺失现象下(随机缺失和分段缺失)不同缺失率的实验结果表明,所提模型无论是在补全精度还是预测精度上都优于目前流行的各类方法。
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