is a tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
| prophet.R (prophet-1.0) | : | prophet.R (prophet-1.1) |
|---|
| | |
| skipping to change at line 19 | | skipping to change at line 19 |
|---|
| "component", "dow", "doy", "holiday", "holidays", "holidays_lower", "generated
_holidays", | | "component", "dow", "doy", "holiday", "holidays", "holidays_lower", "generated
_holidays", |
| "holidays_upper", "ix", "lower", "n", "stat", "trend", "row_number", "extra_re
gressors", "col", | | "holidays_upper", "ix", "lower", "n", "stat", "trend", "row_number", "extra_re
gressors", "col", |
| "trend_lower", "trend_upper", "upper", "value", "weekly", "weekly_lower", "wee
kly_upper", | | "trend_lower", "trend_upper", "upper", "value", "weekly", "weekly_lower", "wee
kly_upper", |
| "x", "yearly", "yearly_lower", "yearly_upper", "yhat", "yhat_lower", "yhat_upp
er", | | "x", "yearly", "yearly_lower", "yearly_upper", "yhat", "yhat_lower", "yhat_upp
er", |
| "country", "year" | | "country", "year" |
| )) | | )) |
| | |
| #' Prophet forecaster. | | #' Prophet forecaster. |
| #' | | #' |
| #' @param df (optional) Dataframe containing the history. Must have columns ds | | #' @param df (optional) Dataframe containing the history. Must have columns ds |
|
| #' (date type) and y, the time series. If growth is logistic, then df must | | #' (date type) and y, the time series. If growth is logistic, then df must |
| #' also have a column cap that specifies the capacity at each ds. If not | | #' also have a column cap that specifies the capacity at each ds. If not |
| #' provided, then the model object will be instantiated but not fit; use | | #' provided, then the model object will be instantiated but not fit; use |
| #' fit.prophet(m, df) to fit the model. | | #' fit.prophet(m, df) to fit the model. |
| #' @param growth String 'linear', 'logistic', or 'flat' to specify a linear, log | | #' @param growth String 'linear', 'logistic', or 'flat' to specify a linear, |
| istic | | #' logistic or flat trend. |
| #' or flat trend. | | |
| #' @param changepoints Vector of dates at which to include potential | | #' @param changepoints Vector of dates at which to include potential |
|
| #' changepoints. If not specified, potential changepoints are selected | | #' changepoints. If not specified, potential changepoints are selected |
| #' automatically. | | #' automatically. |
| #' @param n.changepoints Number of potential changepoints to include. Not used | | #' @param n.changepoints Number of potential changepoints to include. Not used |
|
| #' if input `changepoints` is supplied. If `changepoints` is not supplied, | | #' if input `changepoints` is supplied. If `changepoints` is not supplied, |
| #' then n.changepoints potential changepoints are selected uniformly from the | | #' then n.changepoints potential changepoints are selected uniformly from the |
| #' first `changepoint.range` proportion of df$ds. | | #' first `changepoint.range` proportion of df$ds. |
| #' @param changepoint.range Proportion of history in which trend changepoints | | #' @param changepoint.range Proportion of history in which trend changepoints |
|
| #' will be estimated. Defaults to 0.8 for the first 80%. Not used if | | #' will be estimated. Defaults to 0.8 for the first 80%. Not used if |
| #' `changepoints` is specified. | | #' `changepoints` is specified. |
| #' @param yearly.seasonality Fit yearly seasonality. Can be 'auto', TRUE, | | #' @param yearly.seasonality Fit yearly seasonality. Can be 'auto', TRUE, FALSE, |
| #' FALSE, or a number of Fourier terms to generate. | | #' or a number of Fourier terms to generate. |
| #' @param weekly.seasonality Fit weekly seasonality. Can be 'auto', TRUE, | | #' @param weekly.seasonality Fit weekly seasonality. Can be 'auto', TRUE, FALSE, |
| #' FALSE, or a number of Fourier terms to generate. | | #' or a number of Fourier terms to generate. |
| #' @param daily.seasonality Fit daily seasonality. Can be 'auto', TRUE, | | #' @param daily.seasonality Fit daily seasonality. Can be 'auto', TRUE, FALSE, |
| #' FALSE, or a number of Fourier terms to generate. | | #' or a number of Fourier terms to generate. |
| #' @param holidays data frame with columns holiday (character) and ds (date | | #' @param holidays data frame with columns holiday (character) and ds (date |
|
| #' type)and optionally columns lower_window and upper_window which specify a | | #' type)and optionally columns lower_window and upper_window which specify a |
| #' range of days around the date to be included as holidays. lower_window=-2 | | #' range of days around the date to be included as holidays. lower_window=-2 |
| #' will include 2 days prior to the date as holidays. Also optionally can have | | #' will include 2 days prior to the date as holidays. Also optionally can have |
| #' a column prior_scale specifying the prior scale for each holiday. | | #' a column prior_scale specifying the prior scale for each holiday. |
| #' @param seasonality.mode 'additive' (default) or 'multiplicative'. | | #' @param seasonality.mode 'additive' (default) or 'multiplicative'. |
| #' @param seasonality.prior.scale Parameter modulating the strength of the | | #' @param seasonality.prior.scale Parameter modulating the strength of the |
|
| #' seasonality model. Larger values allow the model to fit larger seasonal | | #' seasonality model. Larger values allow the model to fit larger seasonal |
| #' fluctuations, smaller values dampen the seasonality. Can be specified for | | #' fluctuations, smaller values dampen the seasonality. Can be specified for |
| #' individual seasonalities using add_seasonality. | | #' individual seasonalities using add_seasonality. |
| #' @param holidays.prior.scale Parameter modulating the strength of the holiday | | #' @param holidays.prior.scale Parameter modulating the strength of the holiday |
|
| #' components model, unless overridden in the holidays input. | | #' components model, unless overridden in the holidays input. |
| #' @param changepoint.prior.scale Parameter modulating the flexibility of the | | #' @param changepoint.prior.scale Parameter modulating the flexibility of the |
|
| #' automatic changepoint selection. Large values will allow many changepoints, | | #' automatic changepoint selection. Large values will allow many changepoints, |
| #' small values will allow few changepoints. | | #' small values will allow few changepoints. |
| #' @param mcmc.samples Integer, if greater than 0, will do full Bayesian | | #' @param mcmc.samples Integer, if greater than 0, will do full Bayesian |
|
| #' inference with the specified number of MCMC samples. If 0, will do MAP | | #' inference with the specified number of MCMC samples. If 0, will do MAP |
| #' estimation. | | #' estimation. |
| #' @param interval.width Numeric, width of the uncertainty intervals provided | | #' @param interval.width Numeric, width of the uncertainty intervals provided |
|
| #' for the forecast. If mcmc.samples=0, this will be only the uncertainty | | #' for the forecast. If mcmc.samples=0, this will be only the uncertainty in |
| #' in the trend using the MAP estimate of the extrapolated generative model. | | #' the trend using the MAP estimate of the extrapolated generative model. If |
| #' If mcmc.samples>0, this will be integrated over all model parameters, | | #' mcmc.samples>0, this will be integrated over all model parameters, which |
| #' which will include uncertainty in seasonality. | | #' will include uncertainty in seasonality. |
| #' @param uncertainty.samples Number of simulated draws used to estimate | | #' @param uncertainty.samples Number of simulated draws used to estimate |
|
| #' uncertainty intervals. Settings this value to 0 or False will disable | | #' uncertainty intervals. Settings this value to 0 or False will disable |
| #' uncertainty estimation and speed up the calculation. | | #' uncertainty estimation and speed up the calculation. |
| | #' @param backend Whether to use the "rstan" or "cmdstanr" backend to fit the |
| | #' model. If not provided, uses the R_STAN_BACKEND environment variable. |
| #' @param fit Boolean, if FALSE the model is initialized but not fit. | | #' @param fit Boolean, if FALSE the model is initialized but not fit. |
| #' @param ... Additional arguments, passed to \code{\link{fit.prophet}} | | #' @param ... Additional arguments, passed to \code{\link{fit.prophet}} |
| #' | | #' |
| #' @return A prophet model. | | #' @return A prophet model. |
| #' | | #' |
| #' @examples | | #' @examples |
| #' \dontrun{ | | #' \dontrun{ |
| #' history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'),
by = 'd'), | | #' history <- data.frame(ds = seq(as.Date('2015-01-01'), as.Date('2016-01-01'),
by = 'd'), |
| #' y = sin(1:366/200) + rnorm(366)/10) | | #' y = sin(1:366/200) + rnorm(366)/10) |
| #' m <- prophet(history) | | #' m <- prophet(history) |
| | |
| skipping to change at line 102 | | skipping to change at line 104 |
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| daily.seasonality = 'auto', | | daily.seasonality = 'auto', |
| holidays = NULL, | | holidays = NULL, |
| seasonality.mode = 'additive', | | seasonality.mode = 'additive', |
| seasonality.prior.scale = 10, | | seasonality.prior.scale = 10, |
| holidays.prior.scale = 10, | | holidays.prior.scale = 10, |
| changepoint.prior.scale = 0.05, | | changepoint.prior.scale = 0.05, |
| mcmc.samples = 0, | | mcmc.samples = 0, |
| interval.width = 0.80, | | interval.width = 0.80, |
| uncertainty.samples = 1000, | | uncertainty.samples = 1000, |
| fit = TRUE, | | fit = TRUE, |
|
| | backend = NULL, |
| ... | | ... |
| ) { | | ) { |
| if (!is.null(changepoints)) { | | if (!is.null(changepoints)) { |
| n.changepoints <- length(changepoints) | | n.changepoints <- length(changepoints) |
| } | | } |
| | |
|
| | if (is.null(backend)) backend <- get_stan_backend() |
| | |
| m <- list( | | m <- list( |
| growth = growth, | | growth = growth, |
| changepoints = changepoints, | | changepoints = changepoints, |
| n.changepoints = n.changepoints, | | n.changepoints = n.changepoints, |
| changepoint.range = changepoint.range, | | changepoint.range = changepoint.range, |
| yearly.seasonality = yearly.seasonality, | | yearly.seasonality = yearly.seasonality, |
| weekly.seasonality = weekly.seasonality, | | weekly.seasonality = weekly.seasonality, |
| daily.seasonality = daily.seasonality, | | daily.seasonality = daily.seasonality, |
| holidays = holidays, | | holidays = holidays, |
| seasonality.mode = seasonality.mode, | | seasonality.mode = seasonality.mode, |
| seasonality.prior.scale = seasonality.prior.scale, | | seasonality.prior.scale = seasonality.prior.scale, |
| changepoint.prior.scale = changepoint.prior.scale, | | changepoint.prior.scale = changepoint.prior.scale, |
| holidays.prior.scale = holidays.prior.scale, | | holidays.prior.scale = holidays.prior.scale, |
| mcmc.samples = mcmc.samples, | | mcmc.samples = mcmc.samples, |
| interval.width = interval.width, | | interval.width = interval.width, |
| uncertainty.samples = uncertainty.samples, | | uncertainty.samples = uncertainty.samples, |
|
| | backend = backend, |
| specified.changepoints = !is.null(changepoints), | | specified.changepoints = !is.null(changepoints), |
| start = NULL, # This and following attributes are set during fitting | | start = NULL, # This and following attributes are set during fitting |
| y.scale = NULL, | | y.scale = NULL, |
| logistic.floor = FALSE, | | logistic.floor = FALSE, |
| t.scale = NULL, | | t.scale = NULL, |
| changepoints.t = NULL, | | changepoints.t = NULL, |
| seasonalities = list(), | | seasonalities = list(), |
| extra_regressors = list(), | | extra_regressors = list(), |
| country_holidays = NULL, | | country_holidays = NULL, |
| stan.fit = NULL, | | stan.fit = NULL, |
| | |
| skipping to change at line 297 | | skipping to change at line 303 |
|---|
| #' | | #' |
| #' @return numeric time difference | | #' @return numeric time difference |
| #' | | #' |
| #' @keywords internal | | #' @keywords internal |
| time_diff <- function(ds1, ds2, units = "days") { | | time_diff <- function(ds1, ds2, units = "days") { |
| return(as.numeric(difftime(ds1, ds2, units = units))) | | return(as.numeric(difftime(ds1, ds2, units = units))) |
| } | | } |
| | |
| #' Prepare dataframe for fitting or predicting. | | #' Prepare dataframe for fitting or predicting. |
| #' | | #' |
|
| #' Adds a time index and scales y. Creates auxillary columns 't', 't_ix', | | #' Adds a time index and scales y. Creates auxiliary columns 't', 't_ix', |
| #' 'y_scaled', and 'cap_scaled'. These columns are used during both fitting | | #' 'y_scaled', and 'cap_scaled'. These columns are used during both fitting |
| #' and predicting. | | #' and predicting. |
| #' | | #' |
| #' @param m Prophet object. | | #' @param m Prophet object. |
| #' @param df Data frame with columns ds, y, and cap if logistic growth. Any | | #' @param df Data frame with columns ds, y, and cap if logistic growth. Any |
| #' specified additional regressors must also be present. | | #' specified additional regressors must also be present. |
| #' @param initialize_scales Boolean set scaling factors in m from df. | | #' @param initialize_scales Boolean set scaling factors in m from df. |
| #' | | #' |
| #' @return list with items 'df' and 'm'. | | #' @return list with items 'df' and 'm'. |
| #' | | #' |
| | |
| skipping to change at line 813 | | skipping to change at line 819 |
|---|
| | |
| #' Dataframe with seasonality features. | | #' Dataframe with seasonality features. |
| #' Includes seasonality features, holiday features, and added regressors. | | #' Includes seasonality features, holiday features, and added regressors. |
| #' | | #' |
| #' @param m Prophet object. | | #' @param m Prophet object. |
| #' @param df Dataframe with dates for computing seasonality features and any | | #' @param df Dataframe with dates for computing seasonality features and any |
| #' added regressors. | | #' added regressors. |
| #' | | #' |
| #' @return List with items | | #' @return List with items |
| #' seasonal.features: Dataframe with regressor features, | | #' seasonal.features: Dataframe with regressor features, |
|
| #' prior.scales: Array of prior scales for each colum of the features | | #' prior.scales: Array of prior scales for each column of the features |
| #' dataframe. | | #' dataframe. |
| #' component.cols: Dataframe with indicators for which regression components | | #' component.cols: Dataframe with indicators for which regression components |
| #' correspond to which columns. | | #' correspond to which columns. |
| #' modes: List with keys 'additive' and 'multiplicative' with arrays of | | #' modes: List with keys 'additive' and 'multiplicative' with arrays of |
| #' component names for each mode of seasonality. | | #' component names for each mode of seasonality. |
| #' | | #' |
| #' @keywords internal | | #' @keywords internal |
| make_all_seasonality_features <- function(m, df) { | | make_all_seasonality_features <- function(m, df) { |
| seasonal.features <- data.frame(row.names = 1:nrow(df)) | | seasonal.features <- data.frame(row.names = 1:nrow(df)) |
| prior.scales <- c() | | prior.scales <- c() |
| | |
| skipping to change at line 1230 | | skipping to change at line 1236 |
|---|
| dat$cap <- rep(0, nrow(history)) # Unused inside Stan | | dat$cap <- rep(0, nrow(history)) # Unused inside Stan |
| kinit <- linear_growth_init(history) | | kinit <- linear_growth_init(history) |
| } else if (m$growth == 'flat') { | | } else if (m$growth == 'flat') { |
| dat$cap <- rep(0, nrow(history)) # Unused inside Stan | | dat$cap <- rep(0, nrow(history)) # Unused inside Stan |
| kinit <- flat_growth_init(history) | | kinit <- flat_growth_init(history) |
| } else if (m$growth == 'logistic') { | | } else if (m$growth == 'logistic') { |
| dat$cap <- history$cap_scaled # Add capacities to the Stan data | | dat$cap <- history$cap_scaled # Add capacities to the Stan data |
| kinit <- logistic_growth_init(history) | | kinit <- logistic_growth_init(history) |
| } | | } |
| | |
|
| if (exists(".prophet.stan.model", where = prophet_model_env)) { | | model <- .load_model(m$backend) |
| model <- get('.prophet.stan.model', envir = prophet_model_env) | | |
| } else { | | |
| model <- stanmodels$prophet | | |
| } | | |
| | |
| stan_init <- function() { | | stan_init <- function() { |
| list(k = kinit[1], | | list(k = kinit[1], |
| m = kinit[2], | | m = kinit[2], |
| delta = array(rep(0, length(m$changepoints.t))), | | delta = array(rep(0, length(m$changepoints.t))), |
| beta = array(rep(0, ncol(seasonal.features))), | | beta = array(rep(0, ncol(seasonal.features))), |
| sigma_obs = 1 | | sigma_obs = 1 |
| ) | | ) |
| } | | } |
| | |
| if (min(history$y) == max(history$y) & | | if (min(history$y) == max(history$y) & |
| (m$growth %in% c('linear', 'flat'))) { | | (m$growth %in% c('linear', 'flat'))) { |
| # Nothing to fit. | | # Nothing to fit. |
| m$params <- stan_init() | | m$params <- stan_init() |
| m$params$sigma_obs <- 0. | | m$params$sigma_obs <- 0. |
| n.iteration <- 1. | | n.iteration <- 1. |
|
| } else if (m$mcmc.samples > 0) { | | |
| args <- list( | | |
| object = model, | | |
| data = dat, | | |
| init = stan_init, | | |
| iter = m$mcmc.samples | | |
| ) | | |
| args <- utils::modifyList(args, list(...)) | | |
| m$stan.fit <- do.call(rstan::sampling, args) | | |
| m$params <- rstan::extract(m$stan.fit) | | |
| n.iteration <- length(m$params$k) | | |
| } else { | | } else { |
|
| args <- list( | | if (m$mcmc.samples > 0) { |
| object = model, | | args <- .stan_args(model, dat, stan_init, m$backend, type = "mcmc", m$mcmc |
| data = dat, | | .samples, ...) |
| init = stan_init, | | model_output <- .sampling(args, m$backend) |
| algorithm = if(dat$T < 100) {'Newton'} else {'LBFGS'}, | | } else { |
| iter = 1e4, | | args <- .stan_args(model, dat, stan_init, m$backend, type = "optimize", .. |
| as_vector = FALSE | | .) |
| ) | | model_output <- .fit(args, m$backend) |
| args <- utils::modifyList(args, list(...)) | | |
| m$stan.fit <- do.call(rstan::optimizing, args) | | |
| if (m$stan.fit$return_code != 0) { | | |
| message( | | |
| 'Optimization terminated abnormally. Falling back to Newton optimizer.' | | |
| ) | | |
| args$algorithm = 'Newton' | | |
| m$stan.fit <- do.call(rstan::optimizing, args) | | |
| } | | } |
|
| m$params <- m$stan.fit$par | | m$stan.fit <- model_output$stan_fit |
| n.iteration <- 1 | | m$params <- model_output$params |
| | n.iteration <- model_output$n_iteration |
| } | | } |
|
| | | |
| # Cast the parameters to have consistent form, whether full bayes or MAP | | # Cast the parameters to have consistent form, whether full bayes or MAP |
| for (name in c('delta', 'beta')){ | | for (name in c('delta', 'beta')){ |
| m$params[[name]] <- matrix(m$params[[name]], nrow = n.iteration) | | m$params[[name]] <- matrix(m$params[[name]], nrow = n.iteration) |
| } | | } |
| # rstan::sampling returns 1d arrays; converts to atomic vectors. | | # rstan::sampling returns 1d arrays; converts to atomic vectors. |
| for (name in c('k', 'm', 'sigma_obs')){ | | for (name in c('k', 'm', 'sigma_obs')){ |
| m$params[[name]] <- c(m$params[[name]]) | | m$params[[name]] <- c(m$params[[name]]) |
| } | | } |
| # If no changepoints were requested, replace delta with 0s | | # If no changepoints were requested, replace delta with 0s |
| if (m$n.changepoints == 0) { | | if (m$n.changepoints == 0) { |
| | |
| skipping to change at line 1670 | | skipping to change at line 1651 |
|---|
| #' @param freq 'day', 'week', 'month', 'quarter', 'year', 1(1 sec), 60(1 minute)
or 3600(1 hour). | | #' @param freq 'day', 'week', 'month', 'quarter', 'year', 1(1 sec), 60(1 minute)
or 3600(1 hour). |
| #' @param include_history Boolean to include the historical dates in the data | | #' @param include_history Boolean to include the historical dates in the data |
| #' frame for predictions. | | #' frame for predictions. |
| #' | | #' |
| #' @return Dataframe that extends forward from the end of m$history for the | | #' @return Dataframe that extends forward from the end of m$history for the |
| #' requested number of periods. | | #' requested number of periods. |
| #' | | #' |
| #' @export | | #' @export |
| make_future_dataframe <- function(m, periods, freq = 'day', | | make_future_dataframe <- function(m, periods, freq = 'day', |
| include_history = TRUE) { | | include_history = TRUE) { |
|
| # For backwards compatability with previous zoo date type, | | # For backwards compatibility with previous zoo date type, |
| if (freq == 'm') { | | if (freq == 'm') { |
| freq <- 'month' | | freq <- 'month' |
| } | | } |
| if (is.null(m$history.dates)) { | | if (is.null(m$history.dates)) { |
| stop('Model must be fit before this can be used.') | | stop('Model must be fit before this can be used.') |
| } | | } |
| dates <- seq(max(m$history.dates), length.out = periods + 1, by = freq) | | dates <- seq(max(m$history.dates), length.out = periods + 1, by = freq) |
| dates <- dates[2:(periods + 1)] # Drop the first, which is max(history$ds) | | dates <- dates[2:(periods + 1)] # Drop the first, which is max(history$ds) |
| if (include_history) { | | if (include_history) { |
| dates <- c(m$history.dates, dates) | | dates <- c(m$history.dates, dates) |
| | | |
| End of changes. 22 change blocks. |
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| 76 lines changed or deleted | | 58 lines changed or added |
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"Fossies" - the Fresh Open Source Software Archive 