Python API

Prophet follows the sklearn model API. We create an instance of the Prophet class and then call its fit and predict methods.

The input to Prophet is always a dataframe with two columns: ds and y. The ds (datestamp) column must contain a date or datetime (either is fine). The y column must be numeric, and represents the measurement we wish to forecast.

As an example, let’s look at a time series of daily page views for the Wikipedia page for Peyton Manning. We scraped this data using the Wikipediatrend package in R. Peyton Manning provides a nice example because it illustrates some of Prophet’s features, like multiple seasonality, changing growth rates, and the ability to model special days (such as Manning’s playoff and superbowl appearances). The CSV is available here.

First we’ll import the data and log-transform the y variable.

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# Python
import pandas as pd
import numpy as np
from fbprophet import Prophet
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# Python
df = pd.read_csv('../examples/example_wp_peyton_manning.csv')
df['y'] = np.log(df['y'])
df.head()
ds y
0 2007-12-10 9.590761
1 2007-12-11 8.519590
2 2007-12-12 8.183677
3 2007-12-13 8.072467
4 2007-12-14 7.893572

We fit the model by instantiated a new Prophet object. Any settings to the forecasting procedure are passed into the constructor. Then you call its fit method and pass in the historical dataframe. Fitting should take 1-5 seconds.

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# Python
m = Prophet()
m.fit(df);

Predictions are then made on a dataframe with a column ds containing the dates for which a prediction is to be made. You can get a suitable dataframe that extends into the future a specified number of days using the helper method Prophet.make_future_dataframe. By default it will also include the dates from the history, so we will see the model fit as well.

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# Python
future = m.make_future_dataframe(periods=365)
future.tail()
ds
3265 2017-01-15
3266 2017-01-16
3267 2017-01-17
3268 2017-01-18
3269 2017-01-19

The predict method will assign each row in future a predicted value which it names yhat. If you pass in historical dates, it will provide an in-sample fit. The forecast object here is a new dataframe that includes a column yhat with the forecast, as well as columns for components and uncertainty intervals.

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# Python
forecast = m.predict(future)
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()
ds yhat yhat_lower yhat_upper
3265 2017-01-15 8.205065 7.488507 8.887731
3266 2017-01-16 8.530088 7.862778 9.223688
3267 2017-01-17 8.317468 7.644606 9.021893
3268 2017-01-18 8.150081 7.462394 8.889095
3269 2017-01-19 8.162015 7.438503 8.877361

You can plot the forecast by calling the Prophet.plot method and passing in your forecast dataframe.

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# Python
m.plot(forecast);

png

If you want to see the forecast components, you can use the Prophet.plot_components method. By default you’ll see the trend, yearly seasonality, and weekly seasonality of the time series. If you include holidays, you’ll see those here, too.

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# Python
m.plot_components(forecast);

png

R API

In R, we use 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.

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# R
library(prophet)
library(dplyr)

First we read in the data and create the outcome variable. As in the Python API, this is a dataframe with columns ds and y, containing the date and numeric value respectively. As above, we use here the log number of views to Petyon Manning’s Wikipedia page, available here.

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# R
df <- read.csv('../examples/example_wp_peyton_manning.csv') %>%
  mutate(y = log(y))

We call the prophet function to fit the model. The first argument is the historical dataframe. Additional arguments control how Prophet fits the data and are described in later pages of this documentation.

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# R
m <- prophet(df)

Predictions are made on a dataframe with a column ds containing the dates for which predictions are to be made. The make_future_dataframe function takes the model object and a number of periods to forecast and produces a suitable dataframe. By default it will also include the historical dates so we can evaluate in-sample fit.

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# R
future <- make_future_dataframe(m, periods = 365)
tail(future)
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             ds
3265 2017-01-14
3266 2017-01-15
3267 2017-01-16
3268 2017-01-17
3269 2017-01-18
3270 2017-01-19

As with most modeling procedures in R, we use the generic predict function to get our forecast. The forecast object is a dataframe with a column yhat containing the forecast. It has additional columns for uncertainty intervals and seasonal components.

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# R
forecast <- predict(m, future)
tail(forecast[c('ds', 'yhat', 'yhat_lower', 'yhat_upper')])
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             ds     yhat yhat_lower yhat_upper
3265 2017-01-14 7.832396   7.140713   8.533132
3266 2017-01-15 8.214232   7.460897   8.918678
3267 2017-01-16 8.539239   7.788240   9.262142
3268 2017-01-17 8.326654   7.615613   9.003147
3269 2017-01-18 8.159337   7.382162   8.889958
3270 2017-01-19 8.171276   7.354854   8.922918

You can use the generic plot function to plot the forecast, by passing in the model and the forecast dataframe.

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# R
plot(m, forecast)

png

You can use the prophet_plot_components function to see the forecast broken down into trend, weekly seasonality, and yearly seasonality.

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# R
prophet_plot_components(m, forecast)

png

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