#!/usr/bin/env python
# coding: utf-8

# In[1]:


from statsmodels.tsa.statespace.varmax import VARMAX
import pandas as pd
import numpy as np

get_ipython().run_line_magic('matplotlib', 'inline')

import matplotlib
import matplotlib.pyplot as plt

from random import random


# In[2]:


cols = ['v1', 'v2']
lst = []
for i in range(100):
    v1 = random()
    v2 = v1 + random()
    row = [v1,v2]
    lst.append(row)
    
df = pd.DataFrame(lst, columns=cols)
df.info()
df.tail(3)


# In[3]:


model = VARMAX(df.values.tolist(), order=(1,1))
model_fit = model.fit(disp=False)

y = pd.DataFrame(model_fit.forecast(), columns=cols)
y.loc[100] = y.loc[0]
y.loc[99] = df.loc[99]
y.loc[0] = [np.nan, np.nan]
y.tail(3)


# In[5]:


plt.plot(df['v1'], 'b')
plt.plot(df['v2'], 'g')

plt.plot(y.tail(2), 'r')

plt.show()


# In[10]:


model_fit.summary()


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