Autoregressive Moving Average (ARMA): Artificial data
In [1]:
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)
Generate some data from an ARMA process:
In [2]:
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.
In [3]:
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.
In [4]:
dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs) y = pd.TimeSeries(y, index=dates) arma_mod = sm.tsa.ARMA(y, order=(2,2)) arma_res = arma_mod.fit(trend='nc', disp=-1)
In [5]:
print(arma_res.summary())
In [6]:
y.tail()
Out[6]:
In [7]:
import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(10,8)) fig = arma_res.plot_predict(start='1999m6', end='2001m5', ax=ax) legend = ax.legend(loc='upper left')
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