Prophet

import pandas as pd
df = pd.read_excel('manning.xlsx')
df.head()

from prophet import Prophet
m = Prophet()
m.fit(df)

future = m.make_future_dataframe(periods=365)
future.tail()

forecast = m.predict(future)
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

m.plot(forecast);

m.plot_components(forecast);

from prophet.plot import add_changepoints_to_plot
fig = m.plot(forecast)
a = add_changepoints_to_plot(fig.gca(), m, forecast)

m = Prophet(changepoints=['2014-01-01'])
forecast = m.fit(df).predict(future)

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

h1 = pd.read_excel('manning.xlsx', sheet_name='playoff')
h1['holiday'] = 'playoff'

h2 = pd.read_excel('manning.xlsx', sheet_name='superbowls')
h2['holiday'] = 'superbowl'

holidays = pd.concat([h1, h2])

m = Prophet(holidays=holidays)
forecast = m.fit(df).predict(future)

m.plot_components(forecast);

result = forecast.query('playoff > 0 or superbowl > 0')
result[['ds', 'yhat']].head()

m = Prophet(holidays=holidays)
m.add_country_holidays(country_name='US')
forecast = m.fit(df).predict(future)

result = forecast.query('`Christmas Day` != 0')
result[['ds', 'Christmas Day', 'yhat']]

m = Prophet(weekly_seasonality=False)

d = pd.to_datetime(df.ds)

df['on_season'] = (d.dt.month < 2) | (d.dt.month > 8)
df['off_season'] = ~df['on_season']

m.add_seasonality(name='weekly_on_season', period=7, fourier_order=3, 
                  condition_name='on_season')

m.add_seasonality(name='weekly_off_season', period=7, fourier_order=3, 
                  condition_name='off_season')

forecast = m.fit(df).predict(future)
fig = m.plot_components(forecast);

df = pd.read_excel('uschange.xlsx')

df.columns = ['ds', 'y', 'Income', 'Production', 'Savings', 'Unemployment']

m = Prophet()
m.add_regressor('Unemployment')
m.add_regressor('Income')

forecast = m.fit(df).predict(df)

from prophet.utilities import regressor_coefficients
regressor_coefficients(m)

from statsmodels.formula.api import ols
ols('y ~ Unemployment + Income', data=df).fit().summary()

df = pd.read_csv('log_visit.csv')
df.y.plot()

df['cap'] = 8.5

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

future = m.make_future_dataframe(periods=1826)
future['cap'] = 8.5

fcst = m.predict(future)
m.plot(fcst);