def setup_class(cls):
nobs = 500
ar = [1, -0.5, 0.1]
ma = [1, 0.7]
dist = lambda n: np.random.standard_t(3, size=n)
np.random.seed(8659567)
x = arma_generate_sample(ar, ma, nobs, sigma=1, distrvs=dist,
burnin=500)
mod = TArma(x)
order = (2, 1)
cls.res_ls = mod.fit(order=order)
cls.res = mod.fit_mle(order=order,
start_params=np.r_[cls.res_ls[0], 5, 1],
method='nm', disp=False)
cls.res1_table = np.array(
[[ 0.46157133, -0.07694534, 0.70051876, 2.88693312, 0.97283396],
[ 0.04957594, 0.04345499, 0.03492473, 0.40854823, 0.05568439],
[ 9.31038915, -1.7706905 , 20.05795605, 7.06632146, 17.47049812],
[ 0. , 0.07661218, 0. , 0. , 0. ],
[ 0.05487968, 0.04213054, 0.03102404, 0.37860956, 0.05228474],
[ 0.04649728, 0.04569133, 0.03990779, 0.44315449, 0.05996759]])
cls.res1_conf_int = np.array([[ 0.36440426, 0.55873839],
[-0.16211556, 0.00822488],
[ 0.63206754, 0.76896998],
[ 2.08619331, 3.68767294],
[ 0.86369457, 1.08197335]])
cls.ls_params = np.array([ 0.43393123, -0.08402678, 0.73293058])
cls.ls_bse = np.array([ 0.0377741 , 0.03567847, 0.02744488])
def setup_class(cls):
nobs = 500
ar = [1, -0.5, 0.1]
ma = [1, 0.7]
dist = lambda n: np.random.standard_t(3, size=n)
np.random.seed(8659567)
x = arma_generate_sample(ar, ma, nobs, sigma=1, distrvs=dist,
burnin=500)
mod = TArma(x)
order = (2, 1)
cls.res_ls = mod.fit(order=order)
cls.res = mod.fit_mle(order=order,
start_params=np.r_[cls.res_ls[0], 5, 1],
method='nm', disp=False)
cls.res1_table = np.array(
[[ 0.46157133, -0.07694534, 0.70051876, 2.88693312, 0.97283396],
[ 0.04957594, 0.04345499, 0.03492473, 0.40854823, 0.05568439],
[ 9.31038915, -1.7706905 , 20.05795605, 7.06632146, 17.47049812],
[ 0. , 0.07661218, 0. , 0. , 0. ],
[ 0.05487968, 0.04213054, 0.03102404, 0.37860956, 0.05228474],
[ 0.04649728, 0.04569133, 0.03990779, 0.44315449, 0.05996759]])
cls.res1_conf_int = np.array([[ 0.36440426, 0.55873839],
[-0.16211556, 0.00822488],
[ 0.63206754, 0.76896998],
[ 2.08619331, 3.68767294],
[ 0.86369457, 1.08197335]])
cls.ls_params = np.array([ 0.43393123, -0.08402678, 0.73293058])
cls.ls_bse = np.array([ 0.0377741 , 0.03567847, 0.02744488])
#decimal 1 corresponds to threshold of 5% difference
assert_almost_equal(resls[0] / d.res.params, 1, decimal=1)
assert_almost_equal(rescm.params[:-1] / d.res.params, 1, decimal=1)
#copied to tsa.tests
plt.figure()
plt.plot(x, 'b-o')
plt.plot(modc.predicted(), 'r-')
plt.figure()
plt.plot(modc.error_estimate)
#plt.show()
from statsmodels.miscmodels.tmodel import TArma
modct = TArma(x)
reslst = modc.fit(order=(1,1))
print(reslst[0])
rescmt = modct.fit_mle(order=(1,1), start_params=[-0.4,0.4, 10, 1.],maxiter=500,
maxfun=500)
print(rescmt.params)
from statsmodels.tsa.arima_model import ARMA
mkf = ARMA(x)
##rkf = mkf.fit((1,1))
##rkf.params
rkf = mkf.fit((1,1), trend='nc')
print(rkf.params)
from statsmodels.tsa.arima_process import arma_generate_sample
from __future__ import print_function
import numpy as np
from statsmodels.tsa.arima_process import arma_generate_sample, ArmaProcess
from statsmodels.miscmodels.tmodel import TArma
from statsmodels.tsa.arima_model import ARMA
nobs = 500
ar = [1, -0.6, -0.1]
ma = [1, 0.7]
dist = lambda n: np.random.standard_t(3, size=n)
np.random.seed(8659567)
x = arma_generate_sample(ar, ma, nobs, sigma=1, distrvs=dist,
burnin=500)
mod = TArma(x)
order = (2, 1)
res = mod.fit(order=order)
res2 = mod.fit_mle(order=order, start_params=np.r_[res[0], 5, 1], method='nm')
print(res[0])
proc = ArmaProcess.from_coeffs(res[0][:order[0]], res[0][:order[1]])
print(ar, ma)
proc.nobs = nobs
# TODO: bug nobs is None, not needed ?, used in ArmaProcess.__repr__
print(proc.ar, proc.ma)
print(proc.ar_roots(), proc.ma_roots())
from statsmodels.tsa.arma_mle import Arma