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 the fixed-length window of the past $\tau$ observations as its input feature vector and predicts the next value in the sequence. In practice, this amounts to training a linear regression with a standard optimization loop—for example, using a learning rate of $0.01$ and training for $5$ epochs. While basic, this approach establishes a baseline performance level using a simple linear combination of past data before moving on to more complex sequence architectures.