Quick Start Guide to Using Prophet

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 2012-05-25

As 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   32.99348   39.97525    42.02262    42.02262 36.40008
#> 2   31.64212   38.03261    41.44858    41.44858 34.90475
#> 3   31.41135   38.29274    41.25723    41.25723 34.57250
#> 4   30.93532   37.45746    41.06588    41.06588 34.26847
#> 5   30.57952   37.28505    40.87454    40.87454 34.00372
#> 6   29.81891   36.81012    40.68319    40.68319 33.46964

You can use the generic plot function to plot the forecast, but you must also pass the model in to be plotted:

plot(m, forecast)

You can plot the components of the forecast using the prophet_plot_components function:

prophet_plot_components(m, forecast)