def acovf_explicit(ar, ma, nobs):
"""add correlation of MA representation explicitely
"""
ir = arma_impulse_response(ar, ma)
acovfexpl = [np.dot(ir[: nobs - t], ir[t:nobs]) for t in range(10)]
return acovfexpl
from scikits.statsmodels.sandbox.tsa.arima import arma_generate_sample, arma_impulse_response
from scikits.statsmodels.sandbox.tsa.arima import arma_acovf, arma_acf, ARIMA
# from movstat import acf, acovf
from scikits.statsmodels.sandbox.tsa import acf, acovf, pacf
ar = [1.0, -0.6]
# ar = [1., 0.]
ma = [1.0, 0.4]
# ma = [1., 0.4, 0.6]
# ma = [1., 0.]
mod = "" #'ma2'
x = arma_generate_sample(ar, ma, 5000)
x_acf = acf(x)[:10]
x_ir = arma_impulse_response(ar, ma)
# print x_acf[:10]
# print x_ir[:10]
# irc2 = np.correlate(x_ir,x_ir,'full')[len(x_ir)-1:]
# print irc2[:10]
# print irc2[:10]/irc2[0]
# print irc2[:10-1] / irc2[1:10]
# print x_acf[:10-1] / x_acf[1:10]
# detrend helper from matplotlib.mlab
def detrend(x, key=None):
if key is None or key == "constant":
return detrend_mean(x)
elif key == "linear":
return detrend_linear(x)
