fb_proph.py

# Loading in the data from before
with open("curr_bitcoin.pickle",'rb') as fp:
    ts = pickle.load(fp)
    
# Resetting the index back so Dates are no longer indexed
ts.reset_index(inplace=True)

# Renaming the columns for use in FB prophet
ts.rename(columns={'Date': 'ds', 'Close': 'y'}, inplace=True)

# Fitting and training
mod = proph(interval_width=0.95)
mod.fit(ts)

# Setting up predictions to be made
future = mod.make_future_dataframe(periods=30, freq='D')
future.tail()

# Making predictions
forecast = mod.predict(future)

# Plotting the model
mod.plot(forecast, uncertainty=True)
plt.title('Facebook Prophet Forecast and Fitting')
plt.show()