Goal¶

This post aims to introduce how to forecast the time series data for daily births dataset using Prophet.

Prohpeht Forecasting Model

$$ y (y) = g(t) + s(t) + h(t) + \epsilon_t $$

$g(t)$: a growth term, which is a trend curve
$s(t)$: a seasonality term, which periodically changes
$h(t)$: a holiday term, which indicates irregular events (given by users)
$\epsilon_t$: an error term

Reference

Generate Quick and Accurate Time Series Forecasts using Facebook’s Prophet (with Python & R codes)
Machine learning mastery - How to Get Started with Deep Learning for Time Series Forecasting (7-Day Mini-Course)

Libraries¶

In [55]:

import pandas as pd
import numpy as np
import fbprophet
from fbprophet.plot import add_changepoints_to_plot
import warnings
import matplotlib.pyplot as plt
%matplotlib inline

Load a female daily births dataset¶

In [30]:

url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-total-female-births.csv"
df = pd.read_csv(url, parse_dates=['Date'], date_parser=pd.to_datetime)
df.columns = ['ds', 'y']
df.head()

Out[30]:

	ds	y
0	1959-01-01	35
1	1959-01-02	32
2	1959-01-03	30
3	1959-01-04	31
4	1959-01-05	44

Visualize the raw data¶

In [40]:

plt.plot(df['ds'], df['y']);
plt.title('Daily Female Births in 1959');

Create a Prophet instance¶

In [76]:

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    m = fbprophet.Prophet(yearly_seasonality=True, daily_seasonality=False, 
                          changepoint_range=0.9, 
                          changepoint_prior_scale=0.5,
                          seasonality_mode='multiplicative')
    m.fit(df);

In [77]:

future = m.make_future_dataframe(periods=50, freq='d')
forecast = m.predict(future)

Visualize¶

By Component¶

In [78]:

m.plot_components(forecast);

Plot raw data with forecast¶

In [79]:

m.plot(forecast);

Change point detection¶

No change is detected.

In [80]:

fig = m.plot(forecast)
a = add_changepoints_to_plot(fig.gca(), m, forecast);