论文标题
混淆：多通道数据分析的卷积转换学习融合框架
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
论文作者
论文摘要
这项工作解决了分析多通道时间序列数据％％的问题。在本文中，我们通过基于最近提出的卷积转换学习的％提出一个无监督的融合框架。每个通道都是通过单独的一维卷积变换来处理的；所有通道的输出都由转换学习的完全连接的层融合。训练程序利用激活功能的近端解释。我们将开发的框架应用于多通道财务数据，以进行股票预测和交易。我们将提出的公式与基准深度时间序列分析网络进行比较。结果表明，我们的方法的结果比相比相比要好得多。
This work addresses the problem of analyzing multi-channel time series data %. In this paper, we by proposing an unsupervised fusion framework based on %the recently proposed convolutional transform learning. Each channel is processed by a separate 1D convolutional transform; the output of all the channels are fused by a fully connected layer of transform learning. The training procedure takes advantage of the proximal interpretation of activation functions. We apply the developed framework to multi-channel financial data for stock forecasting and trading. We compare our proposed formulation with benchmark deep time series analysis networks. The results show that our method yields considerably better results than those compared against.
