prophet/python/stan/win/prophet_logistic_growth.stan
Rolando (Max) Espinoza e33e7c4b37 Make stan code windows-compatible. (#96)
The vector/matrix operations fail to compile in windows due to eigen
incompatibility with windows compiler. Here we opt to use a non-
vectorized model for windows platform.
2017-03-09 18:47:59 +02:00

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data {
int T; // Sample size
int<lower=1> K; // Number of seasonal vectors
real t[T]; // Day
real cap[T]; // Capacities
real y[T]; // Time-series
int S; // Number of changepoints
real A[T, S]; // Split indicators
real t_change[S]; // Index of changepoints
real X[T,K]; // season vectors
real<lower=0> sigma; // scale on seasonality prior
real<lower=0> tau; // scale on changepoints prior
}
parameters {
real k; // Base growth rate
real m; // offset
real delta[S]; // Rate adjustments
real<lower=0> sigma_obs; // Observation noise (incl. seasonal variation)
real beta[K]; // seasonal vector
}
transformed parameters {
real gamma[S]; // adjusted offsets, for piecewise continuity
real k_s[S + 1]; // actual rate in each segment
real m_pr;
// Compute the rate in each segment
k_s[1] = k;
for (i in 1:S) {
k_s[i + 1] = k_s[i] + delta[i];
}
// Piecewise offsets
m_pr = m; // The offset in the previous segment
for (i in 1:S) {
gamma[i] = (t_change[i] - m_pr) * (1 - k_s[i] / k_s[i + 1]);
m_pr = m_pr + gamma[i]; // update for the next segment
}
}
model {
real Y[T];
//priors
k ~ normal(0, 5);
m ~ normal(0, 5);
delta ~ double_exponential(0, tau);
sigma_obs ~ normal(0, 0.1);
beta ~ normal(0, sigma);
// Likelihood
for (i in 1:T) {
Y[i] = cap[i] / (1 + exp(-(k + dot_product(A[i], delta)) * (t[i] - (m + dot_product(A[i], gamma))))) + dot_product(X[i], beta);
}
y ~ normal(Y, sigma_obs);
}