Fitting multivariate autoregressive (AR) models is fundamental for analysis of time-series data in a wide range of applications in science, engineering, econometrics, signal processing, and data-science. This dissertation considers the problem of learning a $p$-lag multivariate AR generalized linear model (GLM). In this model, the state of the time-series at each time step, conditioned on the history, is drawn from an exponential family distribution with the mean parameter depending on a linear combination of the last $p$ states. The problem is to learn the linear connectivity tensor from a single observed trajectory of the time-series. We provide non-asymptotic error bounds on the regularized Maximum Likelihood estimator in high dimensions.