jroberayalas

results <- vector(mode = 'numeric', length = nrow(prophetGrid))

# Search best parameters
for (i in seq_len(nrow(prophetGrid))) {
    parameters <- prophetGrid[i, ]
    if (parameters$growth == 'logistic') {train$cap <- parameters$capacity}
    
    m <- prophet(train, growth = parameters$growth, holidays = holidays,
                 seasonality.prior.scale = parameters$seasonality_prior_scale, 
                 changepoint.prior.scale = parameters$changepoint_prior_scale,

jroberayalas

# Search grid
prophetGrid <- expand.grid(changepoint_prior_scale = c(0.05, 0.5, 0.001),
                           seasonality_prior_scale = c(100, 10, 1),
                           holidays_prior_scale = c(100, 10, 1),
                           capacity = c(6043, 6500, 7000, 8000),
                           growth = 'logistic')

jroberayalas

holidays <- filter(bikes, holiday == 1) %>% 
    select(datetime) %>%
    distinct()
holidays$holiday = c('Martin Luther King', 'Emancipation Day', 'Independence Day',
                     'Labor Day', 'Columbus Day', 'Veterans Day', 'New Year', 
                     'Martin Luther King', 'Emancipation Day', 'Independence Day',
                     'Labor Day', 'Columbus Day', 'Veterans Day')
names(holidays) <- c('ds', 'holiday')

jroberayalas

# Libraries
library(tidyverse)
library(lubridate)
library(prophet)
library(forecast)

# Read data
bikes <- read_csv('bikes.csv') %>%
    mutate(datetime = date(datetime))