A standard linear regression model can serve as a simple baseline for sequence prediction by treating the data as if it satisfies a $\tau^{\textrm{th}}$-order Markov condition. The model accepts a fixed-length window of the past $\tau$ observations as its input feature vector and predicts the next value in the sequence. The model is trained using standard optimization (such as gradient descent) to minimize a regression loss like mean squared error. While simple, this approach establishes a baseline performance level using a linear combination of past observations before employing more complex sequence models.