"""
Autoregressive Moving Average (ARMA) Model
"""
import numpy as np
import statsmodels.api as sm

# Generate some data from an ARMA process
from statsmodels.tsa.arima_process import arma_generate_sample

arparams = np.array([.75, -.25])
maparams = np.array([.65, .35])

# 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.
arparams = np.r_[1, -arparams]
maparam = np.r_[1, maparams]
nobs = 250
y = arma_generate_sample(arparams, maparams, nobs)

# Now, optionally, we can add some dates information. For this example,
# we'll use a pandas time series.
import pandas
dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)
y = pandas.TimeSeries(y, index=dates)
arma_mod = sm.tsa.ARMA(y, freq='M')
arma_res = arma_mod.fit(order=(2,2), trend='nc', disp=-1)

"""
Autoregressive Moving Average (ARMA) Model
"""
import numpy as np
import statsmodels.api as sm

# Generate some data from an ARMA process
from statsmodels.tsa.arima_process import arma_generate_sample

np.random.seed(12345)
arparams = np.array([.75, -.25])
maparams = np.array([.65, .35])

# 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.
arparams = np.r_[1, -arparams]
maparam = np.r_[1, maparams]
nobs = 250
y = arma_generate_sample(arparams, maparams, nobs)

# Now, optionally, we can add some dates information. For this example,
# we'll use a pandas time series.
import pandas
dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)
y = pandas.TimeSeries(y, index=dates)
arma_mod = sm.tsa.ARMA(y, freq='M')
arma_res = arma_mod.fit(order=(2,2), trend='nc', disp=-1)

Add seed to example and use stationary coefs