Agenda

• What is Prophet
• Features

Prophet

• Open Source (CRAN and PyPI)
• Time Series forecasting with model fitting in STAN
• CRAN Package : install.packages(‘prophet’)
• Start here https://facebookincubator.github.io/prophet/
  • Intro for use in R and Python
  • Read the paper

Capabilities

• Optimized for business forecasts
  • seasonality, irregular holidays, historical trend changes, growth saturation points
• Customizable in ways intuitive to non-experts
  • specify irregular holiday schedules, growth capacities, tweak parameters to fit
• Best with daily data with min one year of historical data.

The Model

y(t) = g(t) + s(t) + h(t) + ε

g(t): growth function : Logistic growth model with non-constant carrying capacity with changepoints or linear growth model with changepoints. Changepoints are used to model changing rates of growth.

s(t): seasonality (weekly or yearly)

h(t): holidays

ε: error

Data for this demo

• Fictional Sales Data
• A mostly 5 day week operation
• Standard US holidays are observed

Data Format

• 2 Columns - ds and y (date and value)
• For a logistic growth model a “cap” field is needed at every row.

##        ds                   y        
##  Min.   :2015-01-01   Min.   :    0  
##  1st Qu.:2015-08-07   1st Qu.:    2  
##  Median :2016-03-13   Median : 9839  
##  Mean   :2016-03-13   Mean   : 7305  
##  3rd Qu.:2016-10-18   3rd Qu.:10925  
##  Max.   :2017-05-25   Max.   :14694

Data Graph

Training Set

Create a training set - let’s use 2 years so we can let it detect growth/seasonality.

df_train<- df[df$ds < as.Date("2017-01-01"),]
summary(df_train)

##        ds                   y        
##  Min.   :2015-01-01   Min.   :    0  
##  1st Qu.:2015-07-02   1st Qu.:    2  
##  Median :2016-01-01   Median :10015  
##  Mean   :2016-01-01   Mean   : 7384  
##  3rd Qu.:2016-07-01   3rd Qu.:11023  
##  Max.   :2016-12-31   Max.   :14694

Forecast Assumptions

• Default assumption: Linear
• Can also specify a logistic growth model. Must provide a cap (historical and future)

Forecast - 30 Days Forward

30 Days forward

m <- prophet(df_train)
future <- make_future_dataframe(m, periods=30) 
forecast <- predict(m,future)

Forecast - 90 Days Forward

90 Days forward

m <- prophet(df_train)
future <- make_future_dataframe(m, periods=90) 
forecast <- predict(m,future)