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This document provides a very brief introduction to the Prophet API. For a detailed guide on using Prophet, please visit the main site at https://facebook.github.io/prophet/.
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.
library(readr)
df <- read_csv('../tests/testthat/data.csv')
#> Rows: 510 Columns: 2
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (1): y
#> date (1): ds
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.We call the prophet function to fit the model. The first
argument is the historical dataframe. Additional arguments control how
Prophet fits the data.
m <- prophet(df)
#> Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.We need to construct a dataframe for prediction. The
make_future_dataframe function takes the model object and a
number of periods to forecast:
future <- make_future_dataframe(m, periods = 365)
head(future)
#> ds
#> 1 2012-05-18
#> 2 2012-05-21
#> 3 2012-05-22
#> 4 2012-05-23
#> 5 2012-05-24
#> 6 2012-05-25As with most modeling procedures in R, we use the generic
predict function to get our forecast:
forecast <- predict(m, future)
head(forecast)
#> ds trend additive_terms additive_terms_lower additive_terms_upper
#> 1 2012-05-18 42.02262 -5.622539 -5.622539 -5.622539
#> 2 2012-05-21 41.44858 -6.543832 -6.543832 -6.543832
#> 3 2012-05-22 41.25723 -6.684730 -6.684730 -6.684730
#> 4 2012-05-23 41.06588 -6.797414 -6.797414 -6.797414
#> 5 2012-05-24 40.87454 -6.870814 -6.870814 -6.870814
#> 6 2012-05-25 40.68319 -7.213554 -7.213554 -7.213554
#> weekly weekly_lower weekly_upper yearly yearly_lower yearly_upper
#> 1 0.8968967 0.8968967 0.8968967 -6.519435 -6.519435 -6.519435
#> 2 0.6338415 0.6338415 0.6338415 -7.177673 -7.177673 -7.177673
#> 3 0.7232570 0.7232570 0.7232570 -7.407987 -7.407987 -7.407987
#> 4 0.8440252 0.8440252 0.8440252 -7.641439 -7.641439 -7.641439
#> 5 1.0054458 1.0054458 1.0054458 -7.876260 -7.876260 -7.876260
#> 6 0.8968967 0.8968967 0.8968967 -8.110451 -8.110451 -8.110451
#> multiplicative_terms multiplicative_terms_lower multiplicative_terms_upper
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> 6 0 0 0
#> yhat_lower yhat_upper trend_lower trend_upper yhat
#> 1 33.12859 40.08802 42.02262 42.02262 36.40008
#> 2 31.51841 38.25386 41.44858 41.44858 34.90475
#> 3 31.51079 38.22438 41.25723 41.25723 34.57250
#> 4 30.96947 37.76340 41.06588 41.06588 34.26847
#> 5 30.28379 37.13246 40.87454 40.87454 34.00372
#> 6 30.18588 37.10008 40.68319 40.68319 33.46964You can use the generic plot function to plot the
forecast, but you must also pass the model in to be plotted:
You can plot the components of the forecast using the
prophet_plot_components function:
prophet_plot_components(m, forecast)
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> ℹ The deprecated feature was likely used in the prophet package.
#> Please report the issue at <https://github.com/facebook/prophet/issues>.
#> This warning is displayed once per session.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.