论文标题
用于分析相关多元时间序列的深层结构模型
A Deep Structural Model for Analyzing Correlated Multivariate Time Series
论文作者
论文摘要
多元时间序列在现实世界应用中通常会遇到，在许多情况下，这些时间序列是密切相关的。在本文中，我们提出了一个深度学习的结构时间序列模型，该模型可以（i）处理相关的多元时间序列输入，（ii）通过明确学习/提取趋势，季节性和事件成分来预测目标的时间序列。通过1D和2D颞CNN和LSTM层次神经网络学习趋势。 CNN-LSTM体系结构可以（i）以自然方式无缝地利用多个相关时间序列之间的依赖性，（ii）提取加权差异特征以获得更好的趋势学习，并且（iii）记住长期顺序模式。季节性组件通过一组傅立叶项的非线函数近似，并且通过编码事件日期的回归器的简单线性函数来学习事件组件。我们通过在各种时间序列数据集的一系列实验中与几种最先进的方法进行了比较，例如对亚马逊AWS简单存储服务（S3）和Elastic Compute Cloud（EC2）帐单的预测以及同一类别中企业股票的关闭价格。
Multivariate time series are routinely encountered in real-world applications, and in many cases, these time series are strongly correlated. In this paper, we present a deep learning structural time series model which can (i) handle correlated multivariate time series input, and (ii) forecast the targeted temporal sequence by explicitly learning/extracting the trend, seasonality, and event components. The trend is learned via a 1D and 2D temporal CNN and LSTM hierarchical neural net. The CNN-LSTM architecture can (i) seamlessly leverage the dependency among multiple correlated time series in a natural way, (ii) extract the weighted differencing feature for better trend learning, and (iii) memorize the long-term sequential pattern. The seasonality component is approximated via a non-liner function of a set of Fourier terms, and the event components are learned by a simple linear function of regressor encoding the event dates. We compare our model with several state-of-the-art methods through a comprehensive set of experiments on a variety of time series data sets, such as forecasts of Amazon AWS Simple Storage Service (S3) and Elastic Compute Cloud (EC2) billings, and the closing prices for corporate stocks in the same category.
