"Fossies" - the Fresh Open Source Software Archive  

Source code changes of the file "R/inst/stan/prophet.stan" between
prophet-0.7.tar.gz and prophet-1.0.tar.gz

About: Prophet is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

prophet.stan  (prophet-0.7)	:	prophet.stan  (prophet-1.0)
skipping to change at line 30		skipping to change at line 30
while ((cp_idx <= S) && (t[i] >= t_change[cp_idx])) {		while ((cp_idx <= S) && (t[i] >= t_change[cp_idx])) {
a_row[cp_idx] = 1;		a_row[cp_idx] = 1;
cp_idx = cp_idx + 1;		cp_idx = cp_idx + 1;
}		}
A[i] = a_row;		A[i] = a_row;
}		}
return A;		return A;
}		}
// Logistic trend functions		// Logistic trend functions
vector logistic_gamma(real k, real m, vector delta, vector t_change, int S) {		vector logistic_gamma(real k, real m, vector delta, vector t_change, int S) {
vector[S] gamma; // adjusted offsets, for piecewise continuity		vector[S] gamma; // adjusted offsets, for piecewise continuity
vector[S + 1] k_s; // actual rate in each segment		vector[S + 1] k_s; // actual rate in each segment
real m_pr;		real m_pr;
// Compute the rate in each segment		// Compute the rate in each segment
k_s = append_row(k, k + cumulative_sum(delta));		k_s = append_row(k, k + cumulative_sum(delta));
// Piecewise offsets		// Piecewise offsets
m_pr = m; // The offset in the previous segment		m_pr = m; // The offset in the previous segment
skipping to change at line 65		skipping to change at line 64
vector t_change,		vector t_change,
int S		int S
) {		) {
vector[S] gamma;		vector[S] gamma;
gamma = logistic_gamma(k, m, delta, t_change, S);		gamma = logistic_gamma(k, m, delta, t_change, S);
return cap .* inv_logit((k + A * delta) .* (t - (m + A * gamma)));		return cap .* inv_logit((k + A * delta) .* (t - (m + A * gamma)));
}		}
// Linear trend function		// Linear trend function
vector linear_trend(		vector linear_trend(
real k,		real k,
real m,		real m,
vector delta,		vector delta,
vector t,		vector t,
matrix A,		matrix A,
vector t_change		vector t_change
) {		) {
return (k + A * delta) .* t + (m + A * (-t_change .* delta));		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 {		data {
int T; // Number of time periods		int T; // Number of time periods
int<lower=1> K; // Number of regressors		int<lower=1> K; // Number of regressors
vector[T] t; // Time		vector[T] t; // Time
vector[T] cap; // Capacities for logistic trend		vector[T] cap; // Capacities for logistic trend
vector[T] y; // Time series		vector[T] y; // Time series
int S; // Number of changepoints		int S; // Number of changepoints
vector[S] t_change; // Times of trend changepoints		vector[S] t_change; // Times of trend changepoints
matrix[T,K] X; // Regressors		matrix[T,K] X; // Regressors
vector[K] sigmas; // Scale on seasonality prior		vector[K] sigmas; // Scale on seasonality prior
real<lower=0> tau; // Scale on changepoints prior		real<lower=0> tau; // Scale on changepoints prior
int trend_indicator; // 0 for linear, 1 for logistic		int trend_indicator; // 0 for linear, 1 for logistic, 2 for flat
vector[K] s_a; // Indicator of additive features		vector[K] s_a; // Indicator of additive features
vector[K] s_m; // Indicator of multiplicative features		vector[K] s_m; // Indicator of multiplicative features
}		}
transformed data {		transformed data {
matrix[T, S] A;		matrix[T, S] A;
A = get_changepoint_matrix(t, t_change, T, S);		A = get_changepoint_matrix(t, t_change, T, S);
}		}
parameters {		parameters {
real k; // Base trend growth rate		real k; // Base trend growth rate
real m; // Trend offset		real m; // Trend offset
vector[S] delta; // Trend rate adjustments		vector[S] delta; // Trend rate adjustments
real<lower=0> sigma_obs; // Observation noise		real<lower=0> sigma_obs; // Observation noise
vector[K] beta; // Regressor coefficients		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 {		model {
//priors		//priors
k ~ normal(0, 5);		k ~ normal(0, 5);
m ~ normal(0, 5);		m ~ normal(0, 5);
delta ~ double_exponential(0, tau);		delta ~ double_exponential(0, tau);
sigma_obs ~ normal(0, 0.5);		sigma_obs ~ normal(0, 0.5);
beta ~ normal(0, sigmas);		beta ~ normal(0, sigmas);
// Likelihood		// Likelihood
if (trend_indicator == 0) {		y ~ normal(
y ~ normal(		trend
linear_trend(k, m, delta, t, A, t_change)
.* (1 + X * (beta .* s_m))		.* (1 + X * (beta .* s_m))
+ X * (beta .* s_a),		+ X * (beta .* s_a),
sigma_obs		sigma_obs
);		);
} else if (trend_indicator == 1) {
y ~ normal(
logistic_trend(k, m, delta, t, cap, A, t_change, S)
.* (1 + X * (beta .* s_m))
+ X * (beta .* s_a),
sigma_obs
);
}
}		}
End of changes. 7 change blocks.
14 lines changed or deleted		22 lines changed or added

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