Forecasting Multilinear Data via Transform-Based Tensor Autoregression
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.
Cite
@article{arxiv.2205.12201,
title = {Forecasting Multilinear Data via Transform-Based Tensor Autoregression},
author = {Jackson Cates and Randy C. Hoover and Kyle Caudle and Cagri Ozdemir and Karen Braman and David Machette},
journal= {arXiv preprint arXiv:2205.12201},
year = {2022}
}