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Time Series Forecasting for Daily Births Dataset by Prophet

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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

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);