def main():
path_example = "../examples"
"""example 1"""
df = read_dataframe(path_examples=path_example, data_idx=4)
model, future, forecasted = forecast(
df, periods=1096,
showflag=True) # uncertainty intervals seem way too wide
df.loc[(df['ds'] > '2010-01-01') & (df['ds'] < '2011-01-01'), 'y'] = None
model_removed = Prophet().fit(df)
fig = model_removed.plot(
model_removed.predict(future)
) # model with missing data. prediction of whole data with future.
fig.set_figheight(18)
fig.set_figwidth(9)
plt.title('prediction (model with missing data)')
plt.show()
"""example 2"""
df2 = read_dataframe(path_examples=path_example, data_idx=5)
model2, future2, forecasted2 = forecast(
df2, periods=1096,
showflag=True) # extreme outlieres in June 2015 mess up estimate.
df2.loc[(df2['ds'] > '2015-06-01') & (df2['ds'] < '2015-06-30'),
'y'] = None
model2_removed = Prophet().fit(df2) # Same approach as previous example
fig = model2_removed.plot(model2_removed.predict(future2))
fig.set_figheight(18)
fig.set_figwidth(9)
plt.title('prediction2 (model with missing data)')
plt.show()
def prophet_forecast(self, train_start, train_end, test_start, test_end):
from sys import platform
if platform == "linux" or platform == "linux2":
from prophet import Prophet # linux
elif platform == "darwin":
from fbprophet import Prophet # OS X
elif platform == "win32":
from fbprophet import Prophet # Windows...
data = self.data.reset_index()
data.rename(columns={'Date': 'ds', self.column: 'y'}, inplace=True)
# FIXME and take user input
size = len(data)
df_train = data.iloc[int(-size * .10):, :]
df_test = data.iloc[int(-size * .20):, :]
model_prophet = Prophet(seasonality_mode='additive')
model_prophet.add_seasonality(name='monthly',
period=30.5,
fourier_order=5)
model_prophet.fit(df_train)
df_future = model_prophet.make_future_dataframe(periods=365)
df_pred = model_prophet.predict(df_future)
model_prophet.plot(df_pred)
plt.tight_layout()
plt.title('Prophet Forecast')
plt.savefig(os.path.join(img_dirp, f'img/prophet_forecast.png'))
model_prophet.plot_components(df_pred)
plt.tight_layout()
plt.savefig(os.path.join(img_dirp, 'img/components.png'))
# merge test set with predicted data and plot accuracy of model's predictions
selected_columns = ['ds', 'yhat_lower', 'yhat_upper', 'yhat']
df_pred = df_pred.loc[:, selected_columns].reset_index(drop=True)
df_test = df_test.merge(df_pred, on=['ds'], how='left')
df_test.ds = pd.to_datetime(df_test.ds)
df_test.set_index('ds', inplace=True)
fig, ax = plt.subplots(1, 1)
ax = sns.lineplot(
data=df_test[['y', 'yhat_lower', 'yhat_upper', 'yhat']])
ax.fill_between(df_test.index,
df_test.yhat_lower,
df_test.yhat_upper,
alpha=0.3)
ax.set(title=f'{self.column} - actual vs. predicted',
xlabel='Date',
ylabel='{self.column}')
plt.tight_layout()
plt.savefig(os.path.join(img_dirp, 'img/actual_v_predicted.png'))
def forecast(df, periods, showflag=True):
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=periods)
predict = m.predict(future)
if showflag:
fig = m.plot(predict)
fig.set_figheight(18)
fig.set_figwidth(9)
plt.title("forcasted")
plt.show()
return m, future, predict
def fit_predict_model(dataframe,
interval_width=0.99,
changepoint_range=0.8):
m = Prophet(daily_seasonality=False,
yearly_seasonality=False,
weekly_seasonality=False,
seasonality_mode='multiplicative',
interval_width=interval_width,
changepoint_range=changepoint_range)
m = m.fit(dataframe)
forecast = m.predict(dataframe)
forecast['fact'] = dataframe['y'].reset_index(drop=True)
print('Displaying Prophet plot')
fig1 = m.plot(forecast)
return forecast
import pandas as pd
from prophet import Prophet
# float_precision='high' required for pd.read_csv to match precision of Rover.read_csv
df = pd.read_csv('examples/example_wp_log_R.csv', float_precision='high')
df['cap'] = 8.5
m = Prophet(growth='logistic')
m.fit(df, seed=123)
future = m.make_future_dataframe(periods=365)
future['cap'] = 8.5
forecast = m.predict(future)
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
m.plot(forecast).savefig('/tmp/py_logistic.png')
m.plot_components(forecast).savefig('/tmp/py_logistic2.png')
import pandas as pd
from prophet import Prophet
# float_precision='high' required for pd.read_csv to match precision of Rover.read_csv
df = pd.read_csv('examples/example_wp_log_peyton_manning.csv',
float_precision='high')
m = Prophet()
m.add_country_holidays(country_name='US')
m.fit(df, seed=123)
future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
m.plot(forecast).savefig('/tmp/py_country_holidays.png')
m.plot_components(forecast).savefig('/tmp/py_country_holidays2.png')
model = Prophet()
model.fit(data_forecast)
st.header('How far into the future to forecast')
with st.echo():
future = model.make_future_dataframe(periods=365)
st.dataframe(future)
st.header('Create the forecasts')
with st.echo():
forecast = model.predict(future)
st.write(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])
st.header('Plot the forecast')
with st.echo():
fig1 = model.plot(forecast)
fig2 = model.plot_components(forecast)
st.write(fig1)
st.write(fig2)
prophet_chart = alt.Chart(data).mark_circle().encode(x='Date',
y='Open',
tooltip=['Date', 'Open'])
st.write(prophet_chart)
st.markdown('----')
path = st.text_input('CSV file path')
if path:
df = pd.read_csv(path)
import pandas as pd
from prophet import Prophet
# float_precision='high' required for pd.read_csv to match precision of Rover.read_csv
df = pd.read_csv('examples/example_yosemite_temps.csv', float_precision='high')
m = Prophet(changepoint_prior_scale=0.01)
m.fit(df, seed=123)
future = m.make_future_dataframe(periods=300, freq='H')
forecast = m.predict(future)
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
m.plot(forecast).savefig('/tmp/py_subdaily.png')
m.plot_components(forecast).savefig('/tmp/py_subdaily2.png')
import pandas as pd
from prophet import Prophet
# float_precision='high' required for pd.read_csv to match precision of Rover.read_csv
df = pd.read_csv('examples/example_air_passengers.csv', float_precision='high')
m = Prophet(seasonality_mode='multiplicative')
m.fit(df, seed=123)
future = m.make_future_dataframe(50, freq='MS')
forecast = m.predict(future)
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
m.plot(forecast).savefig('/tmp/py_multiplicative_seasonality.png')
m.plot_components(forecast).savefig('/tmp/py_multiplicative_seasonality2.png')
# Общий вариант
df2 = df2.rename(columns={'payment_day': 'Days'})
df2['Days'] = pd.DatetimeIndex(df2['Days'])
# Визуализация текущих данных
ax = df2.set_index('Days').plot(figsize=(12, 8))
ax.set_ylabel("Payment value")
ax.set_xlabel('Date')
plt.show()
# Прогнозирование
my_model = Prophet(interval_width=0.95)
my_model.fit(df2)
future_dates = my_model.make_future_dataframe(periods=30, freq="day")
print(future_dates.tail())
forecast = my_model.predict(future_dates)
# визуализации прогнозируемых данных
# Prophet показывает значения временных рядов (черные точки),
# прогнозируемые значения (синяя линия)
# и интервалы неопределенности прогнозов (синие заштрихованные области).
my_model.plot(forecast, uncertainty=True)
# компоненты прогнозов
my_model.plot_components(forecast)
df.drop_duplicates(inplace=True)
if not df.index.is_monotonic_increasing:
df.sort_index(inplace=True)
# Create Price value
df['y'] = (df[f'open'] + df[f'close']) / 2
# Resample data by by the mean of the day
df_daily = df.resample(interval).mean()
# (2) Prepare Data : ds:y
df_daily['ds'] = df_daily.index
df_daily_prophet = df_daily[['ds', 'y']]
df_daily_prophet = df_daily_prophet.reset_index()
df_daily_prophet = df_daily_prophet.drop(columns=['time'])
m = Prophet()
m.fit(df_daily_prophet)
print(f'Model is trained on {len(df_daily_prophet)} data')
future = m.make_future_dataframe(periods=10)
# Python
forecast = m.predict(future)
predictions = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(10)
predictions.to_csv(name + '.csv')
fig1 = m.plot(forecast)
plt.title(name)
plt.savefig(name)
plt.show()
fig2 = m.plot_components(forecast)
plt.title(name + 'Component')
plt.show()
import pandas as pd
from prophet import Prophet
# float_precision='high' required for pd.read_csv to match precision of Rover.read_csv
df = pd.read_csv('examples/example_wp_log_peyton_manning.csv',
float_precision='high')
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 = m.make_future_dataframe(periods=365)
future['nfl_sunday'] = future['ds'].apply(nfl_sunday)
forecast = m.predict(future)
m.plot(forecast).savefig('/tmp/py_regressors.png')
m.plot_components(forecast).savefig('/tmp/py_regressors2.png')
import pandas as pd
from prophet import Prophet
# float_precision='high' required for pd.read_csv to match precision of Rover.read_csv
df = pd.read_csv('examples/example_wp_log_peyton_manning.csv',
float_precision='high')
m = Prophet(growth='flat')
m.fit(df, seed=123)
print(m.params)
future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
m.plot(forecast).savefig('/tmp/py_flat.png')
m.plot_components(forecast).savefig('/tmp/py_flat2.png')
import pandas as pd
from prophet import Prophet
# float_precision='high' required for pd.read_csv to match precision of Rover.read_csv
df = pd.read_csv('examples/example_wp_log_peyton_manning.csv', float_precision='high')
m = Prophet(weekly_seasonality=False)
m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
m.fit(df, seed=123)
future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
m.plot(forecast).savefig('/tmp/py_custom_seasonality.png')
m.plot_components(forecast).savefig('/tmp/py_custom_seasonality2.png')
#FORECAST OF PROPERTY SALES FOR FIRST HALF #Libraries import pandas as pd from prophet import Prophet #Dataset df1 = datasets[3] #Change to date to datetime df1['date'] = pd.to_datetime(df1['date']) #Rename Columns for Forecast df1.columns = ['ds', 'y'] #Create Instance of Prophet p = Prophet(interval_width=0.92, daily_seasonality=True) #Training The Model model = p.fit(df1) #Make Predections future = p.make_future_dataframe(periods=150, freq='D') forecast_prediction = p.predict(future) #Visualize Predection plot1 = p.plot(forecast_prediction) #FORECAST COMOPONENTS IF NEEDED #plot2 = p.plot_components(forecast_prediction)
import pandas as pd
from prophet import Prophet
# float_precision='high' required for pd.read_csv to match precision of Rover.read_csv
df = pd.read_csv('examples/example_wp_log_peyton_manning.csv',
float_precision='high')
m = Prophet()
m.fit(df, seed=123)
future = m.make_future_dataframe(periods=365)
forecast = m.predict(future)
print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail())
m.plot(forecast).savefig('/tmp/py_linear.png')
m.plot_components(forecast).savefig('/tmp/py_linear2.png')
