  The spatio-temporal autoregressive moving average (STARMA) model is
frequently used in several studies of multivariate time series data, where the
assumption of stationarity is important, but it is not always guaranteed in
practice. One way to proceed is to consider locally stationary processes. In
this paper we propose a time-varying spatio-temporal autoregressive and moving
average (tvSTARMA) modelling based on the locally stationarity assumption. The
time-varying parameters are expanded as linear combinations of wavelet bases
and procedures are proposed to estimate the coefficients. Some simulations and
an application to historical daily precipitation records of Midwestern states
of the USA are illustrated.
