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- ---
- title: "Quick Start Guide to Using Prophet"
- author: "Sean J. Taylor and Ben Letham"
- date: "`r Sys.Date()`"
- output: rmarkdown::html_vignette
- vignette: >
- %\VignetteIndexEntry{Quick Start Guide to Using Prophet}
- %\VignetteEngine{knitr::rmarkdown}
- \usepackage[utf8]{inputenc}
- ---
- ```{r, echo = FALSE, message = FALSE}
- knitr::opts_chunk$set(collapse = T, comment = "#>")
- options(tibble.print_min = 4L, tibble.print_max = 4L)
- library(prophet)
- library(dplyr)
- ```
- Prophet uses the normal model fitting API. We provide a `prophet` function that performs fitting and returns a model object. You can then call `predict` and `plot` on this model object.
- First we read in the data and create the outcome variable.
- ```{r, results= "hide"}
- library(readr)
- df <- read_csv('../tests/testthat/data.csv')
- ```
- We call the `prophet` function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data.
- ```{r}
- m <- prophet(df)
- ```
- We need to construct a dataframe for prediction. The `make_future_dataframe` function takes the model object and a number of periods to forecast:
- ```{r}
- future <- make_future_dataframe(m, periods = 365)
- head(future)
- ```
- As with most modeling procedures in R, we use the generic `predict` function to get our forecast:
- ```{r}
- forecast <- predict(m, future)
- head(forecast)
- ```
- You can use the generic `plot` function to plot the forecast, but you must also pass the model in to be plotted:
- ```{r}
- plot(m, forecast)
- ```
- Just as in Python, you can plot the components of the forecast. In R, you use the `prophet_plot_components` function instead of an instance method:
- ```{r}
- prophet_plot_components(m, forecast)
- ```
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