Hands-On Machine Learning for Algorithmic Trading

How to build autoregressive models

An AR model of order $p$ aims to capture the linear dependence between time series values at different lags and can be written as follows:

This closely resembles a multiple linear regression on lagged values of y_t. This model has the following characteristic equation:

The inverses of the solution to this equation in x are the characteristic roots, and the AR(p) process is stationary if these roots are all less than 1 in absolute terms, and unstable otherwise. For a stationary series, multi-step forecasts will ...