some new features

.venv/lib/python3.12/site-packages/prophet/stan_model/prophet.stan

@@ -0,0 +1,143 @@
+// Copyright (c) Facebook, Inc. and its affiliates.
+
+// This source code is licensed under the MIT license found in the
+// LICENSE file in the root directory of this source tree.
+
+functions {
+  matrix get_changepoint_matrix(vector t, vector t_change, int T, int S) {
+    // Assumes t and t_change are sorted.
+    matrix[T, S] A;
+    row_vector[S] a_row;
+    int cp_idx;
+
+    // Start with an empty matrix.
+    A = rep_matrix(0, T, S);
+    a_row = rep_row_vector(0, S);
+    cp_idx = 1;
+
+    // Fill in each row of A.
+    for (i in 1:T) {
+      while ((cp_idx <= S) && (t[i] >= t_change[cp_idx])) {
+        a_row[cp_idx] = 1;
+        cp_idx = cp_idx + 1;
+      }
+      A[i] = a_row;
+    }
+    return A;
+  }
+
+  // Logistic trend functions
+
+  vector logistic_gamma(real k, real m, vector delta, vector t_change, int S) {
+    vector[S] gamma;  // adjusted offsets, for piecewise continuity
+    vector[S + 1] k_s;  // actual rate in each segment
+    real m_pr;
+
+    // Compute the rate in each segment
+    k_s = append_row(k, k + cumulative_sum(delta));
+
+    // 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
+    }
+    return gamma;
+  }
+
+  vector logistic_trend(
+    real k,
+    real m,
+    vector delta,
+    vector t,
+    vector cap,
+    matrix A,
+    vector t_change,
+    int S
+  ) {
+    vector[S] gamma;
+
+    gamma = logistic_gamma(k, m, delta, t_change, S);
+    return cap .* inv_logit((k + A * delta) .* (t - (m + A * gamma)));
+  }
+
+  // Linear trend function
+
+  vector linear_trend(
+    real k,
+    real m,
+    vector delta,
+    vector t,
+    matrix A,
+    vector t_change
+  ) {
+    return (k + A * delta) .* t + (m + A * (-t_change .* delta));
+  }
+
+  // Flat trend function
+
+  vector flat_trend(
+    real m,
+    int T
+  ) {
+    return rep_vector(m, T);
+  }
+}
+
+data {
+  int T;                // Number of time periods
+  int<lower=1> K;       // Number of regressors
+  vector[T] t;          // Time
+  vector[T] cap;        // Capacities for logistic trend
+  vector[T] y;          // Time series
+  int S;                // Number of changepoints
+  vector[S] t_change;   // Times of trend changepoints
+  matrix[T,K] X;        // Regressors
+  vector[K] sigmas;     // Scale on seasonality prior
+  real<lower=0> tau;    // Scale on changepoints prior
+  int trend_indicator;  // 0 for linear, 1 for logistic, 2 for flat
+  vector[K] s_a;        // Indicator of additive features
+  vector[K] s_m;        // Indicator of multiplicative features
+}
+
+transformed data {
+  matrix[T, S] A = get_changepoint_matrix(t, t_change, T, S);
+  matrix[T, K] X_sa = X .* rep_matrix(s_a', T);
+  matrix[T, K] X_sm = X .* rep_matrix(s_m', T);
+}
+
+parameters {
+  real k;                   // Base trend growth rate
+  real m;                   // Trend offset
+  vector[S] delta;          // Trend rate adjustments
+  real<lower=0> sigma_obs;  // Observation noise
+  vector[K] beta;           // Regressor coefficients
+}
+
+transformed parameters {
+  vector[T] trend;
+  if (trend_indicator == 0) {
+    trend = linear_trend(k, m, delta, t, A, t_change);
+  } else if (trend_indicator == 1) {
+    trend = logistic_trend(k, m, delta, t, cap, A, t_change, S);
+  } else if (trend_indicator == 2) {
+    trend = flat_trend(m, T);
+  }
+}
+
+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_id_glm(
+    X_sa,
+    trend .* (1 + X_sm * beta),
+    beta,
+    sigma_obs
+  );
+}