ARIMA/Using Pyflux.py at master · abal6725/ARIMA · GitHub

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### Using Pyflux

import numpy as np
import pandas as pd
import pyflux as pf
from datetime import datetime
import matplotlib.pyplot as plt
%matplotlib inline

data = pd.read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/MASS/drivers.csv")
data.index = data['time'];
data.loc[(data['time']>=1983.05), 'seat_belt'] = 1;
data.loc[(data['time']<1983.05), 'seat_belt'] = 0;
data.loc[(data['time']>=1974.00), 'oil_crisis'] = 1;
data.loc[(data['time']<1974.00), 'oil_crisis'] = 0;
plt.figure(figsize=(15,5));
plt.plot(data.index,data['drivers']);
plt.ylabel('Driver Deaths');
plt.title('Deaths of Car Drivers in Great Britain 1969-84');
plt.plot();


model = pf.ARIMAX(data=data, formula='drivers~1+seat_belt+oil_crisis',
                  ar=1, ma=1, family=pf.Normal())
x = model.fit("MLE")
x.summary()


import numpy as np
import pandas as pd
from scipy.stats import norm
import statsmodels.api as sm
import matplotlib.pyplot as plt
from datetime import datetime
import requests
from io import BytesIO


# Dataset
friedman2 = requests.get('http://www.stata-press.com/data/r12/friedman2.dta').content
data = pd.read_stata(BytesIO(friedman2))
data.index = data.time

# Variables
endog = data.loc['1959':'1981', 'consump']
exog = sm.add_constant(data.loc['1959':'1981', 'm2'])

# Fit the model
mod = sm.tsa.statespace.SARIMAX(endog, exog, order=(1,0,1))
res = mod.fit(disp=False)
print(res.summary())