A Deep Structural Model for Analyzing Correlated Multivariate Time Series

Changwei Hu, Yifan Hu, Sungyong Seo
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引用次数: 4

Abstract

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.
一种分析相关多元时间序列的深层结构模型
在实际应用程序中经常遇到多元时间序列,并且在许多情况下,这些时间序列是强相关的。在本文中,我们提出了一个深度学习结构时间序列模型,它可以(i)处理相关的多变量时间序列输入,(ii)通过明确地学习/提取趋势、季节性和事件成分来预测目标时间序列。通过一维和二维时间CNN和LSTM分层神经网络学习趋势。CNN-LSTM架构可以(i)以自然的方式无缝地利用多个相关时间序列之间的依赖关系,(ii)提取加权差分特征以更好地进行趋势学习,(iii)记忆长期序列模式。季节性成分通过一组傅立叶项的非线性函数来近似,事件成分通过一个简单的线性回归函数来学习,回归函数编码事件日期。我们通过对各种时间序列数据集的综合实验,将我们的模型与几种最先进的方法进行比较,例如对亚马逊AWS简单存储服务(S3)和弹性计算云(EC2)账单的预测,以及同一类别公司股票的收盘价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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