Forecasting Multilinear Data via Transform-Based Tensor Autoregression

• URL: http://arxiv.org/abs/2205.12201v1
• Date: Tue, 24 May 2022 16:56:35 GMT
• Title: Forecasting Multilinear Data via Transform-Based Tensor Autoregression
• Authors: Jackson Cates, Randy C. Hoover, Kyle Caudle, Cagri Ozdemir, Karen Braman, David Machette
• Abstract summary: This research aims to accomplish these goals through the combination of time-series and multilinear systems. We expand previous autoregressive techniques to forecast multilinear data, aptly named the L-Transform autoregressive (L-TAR) for short. We present an experimental validation of the proposed methods on datasets containing image collections, video sequences, sea surface temperature measurements, stock prices, and networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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