superbowls = pd.DataFrame({
'holiday':
'superbowl',
'ds':
pd.to_datetime(['2010-02-07', '2014-02-02', '2016-02-07']),
'lower_window':
0,
'upper_window':
1,
})
holidays = pd.concat((playoffs, superbowls))
m = Prophet(holidays=holidays)
m.fit(df)
future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)
#print(forecast)
# 通过 forecast 数据框，展示节假日效应
forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][[
'ds', 'playoff', 'superbowl'
]][-10:]
# 可以使用 plot_forecast_component（从fbprophet.plot导入）来画出独立的节假日的成分
from fbprophet.plot import plot_forecast_component
m.plot_forecast_component(forecast, 'superbowl')
# In[ ]:
# 预测的成分分析绘图，展示预测中的趋势、周效应和年度效应
model.plot_components(forecast)
print(forecast.columns)
def test_holiday():
playoffs = pd.DataFrame({
'holiday': 'playoff',
'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16',
'2010-01-24', '2010-02-07', '2011-01-08',
'2013-01-12', '2014-01-12', '2014-01-19',
'2014-02-02', '2015-01-11', '2016-01-17',
'2016-01-24', '2016-02-07']),
'lower_window': 0,
'upper_window': 1,
})
superbowls = pd.DataFrame({
'holiday': 'superbowl',
'ds': pd.to_datetime(['2010-02-07', '2014-02-02', '2016-02-07']),
'lower_window': 0,
'upper_window': 1,
})
holidays = pd.concat((playoffs, superbowls))
df = pd.read_csv(prophet_path + os.sep + 'example_wp_log_peyton_manning.csv')
m = Prophet(holidays=holidays)
m.fit(df)
future = m.make_future_dataframe(periods=30)
forecast = m.predict(future)
# forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][['ds', 'playoff', 'superbowl']][-10:]
fig = m.plot_components(forecast)
m.plot(forecast)
m.plot_forecast_component(forecast, 'superbowl')
# Prophet uses a Fourier order of 3 for weekly seasonality and 10 for yearly seasonality.
m.plot_yearly()
# 越高越多变
# N Fourier terms corresponds to 2N variables used for modeling the cycle
m = Prophet(yearly_seasonality=20).fit(df)
a = m.plot_yearly()
# 添加月的周期性
m = Prophet(weekly_seasonality=False)
m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
forecast = m.fit(df).predict(future)
fig = m.plot_components(forecast)
# holidays_prior_scale. By default this parameter is 10, holidays are overfitting?
m = Prophet(holidays=holidays, holidays_prior_scale=0.05).fit(df)
forecast = m.predict(future)
# forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][['ds', 'playoff', 'superbowl']][-10:]
# seasonality_prior_scale
m = Prophet()
m.add_seasonality(name='weekly', period=7, fourier_order=3, prior_scale=0.1)
# Add some func
def nfl_sunday(ds):
date = pd.to_datetime(ds)
if date.weekday() == 6 and (date.month > 8 or date.month < 2):
return 1
else:
return 0
df['nfl_sunday'] = df['ds'].apply(nfl_sunday)
m = Prophet()
m.add_regressor('nfl_sunday')
m.fit(df)
future['nfl_sunday'] = future['ds'].apply(nfl_sunday)
forecast = m.predict(future)
fig = m.plot_components(forecast)
