Dynamic linear models refers to modeling problems where coefficients (as in regression) are allowed to vary with time. This is the so called state-space approach.
A dynamic linear model (DLM) is a state space approach to time series modeling. The general form of normal DLMs for univariate time series of equally spaced observations takes the following form:
$${\bf y}_t = {\bf F'}_t{\bf \theta}_t + {\bf \nu}_t$$ $${\bf \theta}_t = {\bf G}_t {\bf \theta}_{t-1} + {\bf w}_t$$
Where $\nu_t \sim N(0,v_t)$ and $w_t \sim N(0, W_t)$ and are IID. One can show that linear regression, AR, ARMA, polynomial regression and Fourier series can be written as special cases of the above general form.
