  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.
