Oil prices forecast

The Python code:

# -*- coding: utf-8 -*-

import pandas as pd

from fbprophet import Prophet

import matplotlib.pyplot as plt

plt.style.use('fivethirtyeight')

# Importing data

df = pd.read_csv('oil_prices.csv')

format = '%d/%m/%Y'

df['ts'] = pd.to_datetime(df['ts'], format=format)

df = df.set_index(pd.DatetimeIndex(df['ts']))

df = df.rename(columns={'ts': 'ds', 'price': 'y'})

# set the uncertainty interval to 95% (the Prophet default is 80%)

my_model = Prophet(n_changepoints=10, interval_width=0.95, daily_seasonality=False, weekly_seasonality=True, yearly_seasonality=True, uncertainty_samples=1000)

forecast_period = 12 # Number of forecasted time steps

my_model.fit(df)

# In order to obtain forecasts of our time series, we must provide Prophet with a new DataFrame

# containing a ds column that holds the dates for which we want predictions:

future_dates = my_model.make_future_dataframe(periods=forecast_period, freq='12MS')

# The DataFrame of future dates is then used as input to the predict method of our fitted model:

forecast = my_model.predict(future_dates)

'''

Prophet returns a large DataFrame with many interesting columns, but we subset our output to the columns most relevant to forecasting, which are:

ds: the datestamp of the forecasted value

yhat: the forecasted value of our metric (in Statistics, yhat is a notation traditionally used to represent the predicted values of a value y)

yhat_lower: the lower bound of our forecasts

yhat_upper: the upper bound of our forecasts

'''

#my_model.plot(forecast, uncertainty=True)

fig = plt.figure()

plt.plot(df['ds'], df['y'])

plt.plot(forecast[-forecast_period:]['ds'], forecast[-forecast_period:]['yhat'])

plt.plot(forecast[-forecast_period:]['ds'], forecast[-forecast_period:]['yhat_upper'], 'k--')

plt.plot(forecast[-forecast_period:]['ds'], forecast[-forecast_period:]['yhat_lower'], 'k--')

plt.title('Brent crude oil price forecast')

plt.xlabel('Date (years)')

plt.ylabel('U.S. dollars per barrel')

plt.legend(['Data','Forecast','Upper limit','Lower limit'])

plt.show()