论文标题
具有多模式不规则时间序列事件的融合框架
Features Fusion Framework for Multimodal Irregular Time-series Events
论文作者
论文摘要
来自多个来源的一些数据可以建模为具有不同采样频率，数据组成，时间关系和特征的多模式时间序列事件。不同类型的事件具有复杂的非线性关系，每个事件的时间都是不规则的。经典的复发性神经网络（RNN）模型和当前最新变压器模型都无法很好地处理这些功能。在本文中，根据长期短期内存网络（LSTM）提出了用于多模式不规则时间序列事件的特征融合框架。首先，根据不同事件的不规则模式提取复杂特征。其次，将复杂特征之间的非线性相关性和复杂的时间依赖关系捕获并融合到张量中。最后，功能门用于控制不同张量的访问频率。 对MIMIC-III数据集进行的广泛实验表明，所提出的框架在AUC（接收器操作特征曲线下的区域）和AP（平均精度）方面显着优于现有方法。
Some data from multiple sources can be modeled as multimodal time-series events which have different sampling frequencies, data compositions, temporal relations and characteristics. Different types of events have complex nonlinear relationships, and the time of each event is irregular. Neither the classical Recurrent Neural Network (RNN) model nor the current state-of-the-art Transformer model can deal with these features well. In this paper, a features fusion framework for multimodal irregular time-series events is proposed based on the Long Short-Term Memory networks (LSTM). Firstly, the complex features are extracted according to the irregular patterns of different events. Secondly, the nonlinear correlation and complex temporal dependencies relationship between complex features are captured and fused into a tensor. Finally, a feature gate are used to control the access frequency of different tensors. Extensive experiments on MIMIC-III dataset demonstrate that the proposed framework significantly outperforms to the existing methods in terms of AUC (the area under Receiver Operating Characteristic curve) and AP (Average Precision).
