

In [1]:

    
%load_ext rpy2.ipython
%matplotlib inline
from fbprophet import Prophet
import pandas as pd
import logging
logging.getLogger('fbprophet').setLevel(logging.ERROR)
import warnings
warnings.filterwarnings("ignore")



In [2]:

    
%%R
library(prophet)

Forecasting Growth

By default, Prophet uses a linear model for its forecast. When forecasting growth, there is usually some maximum achievable point: total market size, total population size, etc. This is called the carrying capacity, and the forecast should saturate at this point.

Prophet allows you to make forecasts using a logistic growth trend model, with a specified carrying capacity. We illustrate this with the log number of page visits to the R (programming language) page on Wikipedia:



In [3]:

    
%%R
df <- read.csv('../examples/example_wp_log_R.csv')



In [4]:

    
df = pd.read_csv('../examples/example_wp_log_R.csv')

We must specify the carrying capacity in a column cap. Here we will assume a particular value, but this would usually be set using data or expertise about the market size.



In [5]:

    
%%R
df$cap <- 8.5



In [6]:

    
df['cap'] = 8.5

The important things to note are that cap must be specified for every row in the dataframe, and that it does not have to be constant. If the market size is growing, then cap can be an increasing sequence.

We then fit the model as before, except pass in an additional argument to specify logistic growth:



In [7]:

    
%%R
m <- prophet(df, growth = 'logistic')









    





Initial log joint probability = -19.9808
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance



In [8]:

    
m = Prophet(growth='logistic')
m.fit(df)









    Out[8]:





<fbprophet.forecaster.Prophet at 0x7f52e797fb90>

We make a dataframe for future predictions as before, except we must also specify the capacity in the future. Here we keep capacity constant at the same value as in the history, and forecast 3 years into the future:



In [9]:

    
%%R -w 10 -h 6 -u in
future <- make_future_dataframe(m, periods = 1826)
future$cap <- 8.5
fcst <- predict(m, future)
plot(m, fcst)



In [10]:

    
future = m.make_future_dataframe(periods=1826)
future['cap'] = 8.5
fcst = m.predict(future)
fig = m.plot(fcst)

The logistic function has an implicit minimum of 0, and will saturate at 0 the same way that it saturates at the capacity. It is possible to also specify a different saturating minimum.

Saturating Minimum

The logistic growth model can also handle a saturating minimum, which is specified with a column floor in the same way as the cap column specifies the maximum:



In [11]:

    
%%R -w 10 -h 6 -u in
df$y <- 10 - df$y
df$cap <- 6
df$floor <- 1.5
future$cap <- 6
future$floor <- 1.5
m <- prophet(df, growth = 'logistic')
fcst <- predict(m, future)
plot(m, fcst)









    





Initial log joint probability = -109.241
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance



In [12]:

    
df['y'] = 10 - df['y']
df['cap'] = 6
df['floor'] = 1.5
future['cap'] = 6
future['floor'] = 1.5
m = Prophet(growth='logistic')
m.fit(df)
fcst = m.predict(future)
fig = m.plot(fcst)

To use a logistic growth trend with a saturating minimum, a maximum capacity must also be specified.