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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Autoregressive Moving Average (ARMA)
Step1:  Generate some data from an ARMA process
Step2:  The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated.
Step3:  Now, optionally, we can add some dates information. For this example, we'll use a pandas time series.
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Python Code:
%matplotlib inline
from __future__ import print_function
import numpy as np
import statsmodels.api as sm
import pandas as pd
from statsmodels.tsa.arima_process import arma_generate_sample
np.random.seed(12345)
Explanation: Autoregressive Moving Average (ARMA): Artificial data
End of explanation
arparams = np.array([.75, -.25])
maparams = np.array([.65, .35])
Explanation: Generate some data from an ARMA process:
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arparams = np.r_[1, -arparams]
maparams = np.r_[1, maparams]
nobs = 250
y = arma_generate_sample(arparams, maparams, nobs)
Explanation: The conventions of the arma_generate function require that we specify a 1 for the zero-lag of the AR and MA parameters and that the AR parameters be negated.
End of explanation
dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)
y = pd.Series(y, index=dates)
arma_mod = sm.tsa.ARMA(y, order=(2,2))
arma_res = arma_mod.fit(trend='nc', disp=-1)
print(arma_res.summary())
y.tail()
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10,8))
fig = arma_res.plot_predict(start='1999-06-30', end='2001-05-31', ax=ax)
legend = ax.legend(loc='upper left')
Explanation: Now, optionally, we can add some dates information. For this example, we'll use a pandas time series.
End of explanation
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