论文标题
使用注意网络的时间序列预测变量的可解释功能工程
Interpretable Feature Engineering for Time Series Predictors using Attention Networks
论文作者
论文摘要
时间序列预测因素的回归问题在银行和许多其他应用领域很常见。在本文中，我们使用多头注意网络来开发可解释的功能，并使用它们来实现良好的预测性能。自定义的注意力层明确使用乘法交互，并构建功能工程头，以简约的方式捕获时间动力学。卷积层用于结合多元时间序列。我们还讨论了在建模过程中处理静态协变量的方法。可视化和解释工具用于解释结果并解释输入和提取特征之间的关系。模拟和实际数据集都用于说明方法的有用性。关键字：注意力头，深度神经网络，可解释的功能工程
Regression problems with time-series predictors are common in banking and many other areas of application. In this paper, we use multi-head attention networks to develop interpretable features and use them to achieve good predictive performance. The customized attention layer explicitly uses multiplicative interactions and builds feature-engineering heads that capture temporal dynamics in a parsimonious manner. Convolutional layers are used to combine multivariate time series. We also discuss methods for handling static covariates in the modeling process. Visualization and explanation tools are used to interpret the results and explain the relationship between the inputs and the extracted features. Both simulation and real dataset are used to illustrate the usefulness of the methodology. Keyword: Attention heads, Deep neural networks, Interpretable feature engineering
