import numpy as np
import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns

palette=sns.color_palette()
sns.set_theme()

df=pd.read_csv('DUQ_hourly.csv',
               index_col='Datetime',
               parse_dates=True)

df.info()

fig,ax=plt.subplots(figsize=(12,8))
plt.title('Duquesne Light Co. Estimated Energe Consumption, 2005-2018',fontsize=16)
df.plot(ax=ax)
plt.ylabel('Energy Consumption (MW)')
plt.legend(['Energy (MW)']);

fig,ax=plt.subplots(figsize=(12,8))
plt.title('Duquesne Light Co. Estimated Energe Consumption, 2008-2010',fontsize=16)
df.loc[(df.index<'1-1-2010') & (df.index>='1-1-2008')].plot(ax=ax)
plt.ylabel('Energy Consumption (MW)')
plt.legend(['Energy (MW)']);

import prophet

train=df.loc[df.index<'1-1-2016']
test=df.loc[df.index>='1-1-2016']

_,ax=plt.subplots()
train.plot(ax=ax)
test.plot(ax=ax,c=palette[1])
plt.legend(['train','test'])

prophet_train=train.reset_index().rename(columns={'Datetime':'ds','DUQ_MW':'y'})

p=prophet.Prophet()
p.fit(prophet_train)

prophet_test=test.reset_index().rename(columns={'Datetime':'ds','DUQ_MW':'y'})

forecast=p.predict(prophet_test)

_,ax=plt.subplots(figsize=(12,8))
test.plot(ax=ax)
forecast['yhat'].plot(ax=ax)

_,ax=plt.subplots(figsize=(12,8))
test.plot(ax=ax,color=palette[1])
p.plot(forecast,ax=ax);

p.plot_components(forecast);

forecast