\begin{definition}[Definition:Autoregressive Model/Parameter]
Let $S$ be a stochastic process based on an equispaced time series.
Let the values of $S$ at timestamps $t, t - 1, t - 2, \dotsc$ be $z_t, z_{t - 1}, z_{t - 2}, \dotsc$
Let $\tilde z_t, \tilde z_{t - 1}, \tilde z_{t - 2}, \dotsc$ be deviations from a constant mean level $\mu$:
:$\tilde z_t = z_t - \mu$
Let $a_t, a_{t - 1}, a_{t - 2}, \dotsc$ be a sequence of independent shocks at timestamps $t, t - 1, t - 2, \dotsc$
Let $M$ be an '''autoregessive model''' on $S$ of order $p$:
:$\tilde z_t = \phi_1 \tilde z_{t - 1} + \phi_2 \tilde z_{t - 2} + \dotsb + \phi_p \tilde z_{t - p} + a_t$
The '''parameters''' of $M$ consist of:
:the constant mean level $\mu$
:the variance $\sigma_a^2$ of the underlying (usually white noise) process of the independent shock $a_t$
:the coefficients $\phi_1$ to $\phi_p$.
In practice, each of these '''parameters''' needs to be estimated from the data.
\end{definition}