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- data {
- int T; // Sample size
- int<lower=1> K; // Number of seasonal vectors
- vector[T] t; // Day
- vector[T] y; // Time-series
- int S; // Number of changepoints
- matrix[T, S] A; // Split indicators
- real t_change[S]; // Index of changepoints
- matrix[T,K] X; // season vectors
- vector[K] sigmas; // scale on seasonality prior
- real<lower=0> tau; // scale on changepoints prior
- }
- parameters {
- real k; // Base growth rate
- real m; // offset
- vector[S] delta; // Rate adjustments
- real<lower=0> sigma_obs; // Observation noise (incl. seasonal variation)
- vector[K] beta; // seasonal vector
- }
- transformed parameters {
- vector[S] gamma; // adjusted offsets, for piecewise continuity
- for (i in 1:S) {
- gamma[i] = -t_change[i] * delta[i];
- }
- }
- model {
- //priors
- k ~ normal(0, 5);
- m ~ normal(0, 5);
- delta ~ double_exponential(0, tau);
- sigma_obs ~ normal(0, 0.5);
- beta ~ normal(0, sigmas);
- // Likelihood
- y ~ normal((k + A * delta) .* t + (m + A * gamma) + X * beta, sigma_obs);
- }
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