A seasonal effect model posits a fixed set of recurring, discrete 'seasons', each of which is active for a fixed number of timesteps and, while active, contributes a different effect to the time series. These are generally not meteorological seasons, but represent regular recurring patterns such as hour-of-day or day-of-week effects. Each season lasts for a fixed number of timesteps. The effect of each season drifts from one occurrence to the next following a Gaussian random walk:
sts_seasonal( observed_time_series = NULL, num_seasons, num_steps_per_season = 1, drift_scale_prior = NULL, initial_effect_prior = NULL, constrain_mean_effect_to_zero = TRUE, name = NULL )
the name of this model component. Default value: 'Seasonal'.
an instance of
effects[season, occurrence[i]] = ( effects[season, occurrence[i-1]] + Normal(loc=0., scale=drift_scale))
drift_scale parameter governs the standard deviation of the random walk;
for example, in a day-of-week model it governs the change in effect from this
Monday to next Monday.