论文标题
通过基于转换的张量自动性预测多线性数据进行预测
Forecasting Multilinear Data via Transform-Based Tensor Autoregression
论文作者
论文摘要
在大数据时代，对分析和预测二维数据的新方法的需求不断增长。当前的研究旨在通过时间序列建模和多线性代数系统的结合来实现这些目标。我们将以前的自回旋技术扩展到预测多线性数据，恰当地命名为L转换张量自动回归（简称L-TAR）。张量分解和多线性张量产品使这种方法是一种可行的预测方法。我们通过可逆的离散线性变换实现了观测列之间的统计独立性，从而实现了鸿沟和征服方法。我们对包含图像集，视频序列，海面温度测量，股票价格和网络的数据集上提出的方法进行了实验验证。
In the era of big data, there is an increasing demand for new methods for analyzing and forecasting 2-dimensional data. The current research aims to accomplish these goals through the combination of time-series modeling and multilinear algebraic systems. We expand previous autoregressive techniques to forecast multilinear data, aptly named the L-Transform Tensor autoregressive (L-TAR for short). Tensor decompositions and multilinear tensor products have allowed for this approach to be a feasible method of forecasting. We achieve statistical independence between the columns of the observations through invertible discrete linear transforms, enabling a divide and conquer approach. We present an experimental validation of the proposed methods on datasets containing image collections, video sequences, sea surface temperature measurements, stock prices, and networks.
