Posts about forecasting

RSS feed

Time Series Forecasting for Daily Births Dataset by Prophet

Comments

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);