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Complex-Valued Group Lasso For Tensor Autoregressive Models

By Yang Hu

Abstract

I will introduce a group lasso algorithm for complex variables and demonstrate its application to a novel time series model called tensor autoregression (T-AR). T-AR utilizes the t-product tensor operation on 3-dimensional tensors and models time series exhibiting seasonality and geometric trend. The tensor structure of T-AR enables historical information from a selection of lag durations to simultaneously affect the prediction of a single series. I will first introduce the topic of tensor computations and motivate the t-product and T-SVD manipulations. Then, I will derive the T-AR model from the t-product definition and discuss its properties and interpretation. Next, I will adapt a group lasso algorithm to complex-valued problems and derive the fast algorithm for lag selection in T-AR. Finally, I will conclude with simulation results.2021-02-0

Year: 2016
OAI identifier: oai:ecommons.cornell.edu:1813/43737
Provided by: eCommons@Cornell
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