def mcarma22(niter=10, nsample=1000, ar=None, ma=None, sig=0.5):
'''run Monte Carlo for ARMA(2,2)
DGP parameters currently hard coded
also sample size `nsample`
was not a self contained function, used instances from outer scope
now corrected
'''
#nsample = 1000
#ar = [1.0, 0, 0]
if ar is None:
ar = [1.0, -0.55, -0.1]
#ma = [1.0, 0, 0]
if ma is None:
ma = [1.0, 0.3, 0.2]
results = []
results_bse = []
for _ in range(niter):
y2 = arma_generate_sample(ar,ma,nsample+1000, sig)[-nsample:]
y2 -= y2.mean()
arest2 = Arma(y2)
rhohat2a, cov_x2a, infodict, mesg, ier = arest2.fit((2,2))
results.append(rhohat2a)
err2a = arest2.geterrors(rhohat2a)
sige2a = np.sqrt(np.dot(err2a,err2a)/nsample)
#print 'sige2a', sige2a,
#print 'cov_x2a.shape', cov_x2a.shape
#results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
if not cov_x2a is None:
results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
else:
results_bse.append(np.nan + np.zeros_like(rhohat2a))
return np.r_[ar[1:], ma[1:]], np.array(results), np.array(results_bse)
def mcarma22(niter=10, nsample=1000, ar=None, ma=None, sig=0.5):
'''run Monte Carlo for ARMA(2,2)
DGP parameters currently hard coded
also sample size `nsample`
was not a self contained function, used instances from outer scope
now corrected
'''
#nsample = 1000
#ar = [1.0, 0, 0]
if ar is None:
ar = [1.0, -0.55, -0.1]
#ma = [1.0, 0, 0]
if ma is None:
ma = [1.0, 0.3, 0.2]
results = []
results_bse = []
for _ in range(niter):
y2 = arma_generate_sample(ar, ma, nsample + 1000, sig)[-nsample:]
y2 -= y2.mean()
arest2 = Arma(y2)
rhohat2a, cov_x2a, infodict, mesg, ier = arest2.fit((2, 2))
results.append(rhohat2a)
err2a = arest2.geterrors(rhohat2a)
sige2a = np.sqrt(np.dot(err2a, err2a) / nsample)
#print 'sige2a', sige2a,
#print 'cov_x2a.shape', cov_x2a.shape
#results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
if not cov_x2a is None:
results_bse.append(sige2a * np.sqrt(np.diag(cov_x2a)))
else:
results_bse.append(np.nan + np.zeros_like(rhohat2a))
return np.r_[ar[1:], ma[1:]], np.array(results), np.array(results_bse)
import numpy as np
from gwstatsmodels.tsa.arima_process import arma_generate_sample
from gwstatsmodels.iolib import savetxt
np.random.seed(12345)
# no constant
y_arma11 = arma_generate_sample([1., -.75], [1., .35], nsample=250)
y_arma14 = arma_generate_sample([1., -.75], [1., .35, -.75, .1, .35],
nsample=250)
y_arma41 = arma_generate_sample([1., -.75, .25, .25, -.75], [1., .35],
nsample=250)
y_arma22 = arma_generate_sample([1., -.75, .45], [1., .35, -.9], nsample=250)
y_arma50 = arma_generate_sample([1., -.75, .35, -.3, -.2, .1], [1.],
nsample=250)
y_arma02 = arma_generate_sample([1.], [1., .35, -.75], nsample=250)
# constant
constant = 4.5
y_arma11c = arma_generate_sample([1., -.75], [1., .35], nsample=250) + constant
y_arma14c = arma_generate_sample([1., -.75], [1., .35, -.75, .1, .35],
nsample=250) + constant
y_arma41c = arma_generate_sample([1., -.75, .25, .25, -.75], [1., .35],
nsample=250) + constant
y_arma22c = arma_generate_sample([1., -.75, .45],[1., .35, -.9], nsample=250) + \
constant
y_arma50c = arma_generate_sample([1., -.75, .35, -.3, -.2, .1], [1.],
nsample=250) + constant
import scikits.talkbox as stb
import scikits.talkbox.spectral.basic as stbs
except:
hastalkbox = False
ar = [1., -0.7]#[1,0,0,0,0,0,0,-0.7]
ma = [1., 0.3]
ar = np.convolve([1.]+[0]*50 +[-0.6], ar)
ar = np.convolve([1., -0.5]+[0]*49 +[-0.3], ar)
n_startup = 1000
nobs = 1000
# throwing away samples at beginning makes sample more "stationary"
xo = arma_generate_sample(ar,ma,n_startup+nobs)
x = xo[n_startup:]
#moved to tsa.arima_process
#def arma_periodogram(ar, ma, **kwds):
# '''periodogram for ARMA process given by lag-polynomials ar and ma
#
# Parameters
# ----------
# ar : array_like
# autoregressive lag-polynomial with leading 1 and lhs sign
# ma : array_like
# moving average lag-polynomial with leading 1
# kwds : options
# options for scipy.signal.freqz
# default: worN=None, whole=0
'''
import numpy as np
from gwstatsmodels.sandbox import tsa
from gwstatsmodels.tsa.arima_process import arma_generate_sample
from maketests_mlabwrap import HoldIt
if __name__ == '__main__':
filen = 'savedrvs_tmp.py'
np.set_printoptions(precision=14, linewidth=100)
# check arma to return same as random.normal
np.random.seed(10000)
xo = arma_generate_sample([1], [1], nsample=100)
xo2 = np.round(xo*1000).astype(int)
np.random.seed(10000)
rvs = np.random.normal(size=100)
rvs2 = np.round(xo*1000).astype(int)
assert (xo2==rvs2).all()
nsample = 1000
data = HoldIt('rvsdata')
np.random.seed(10000)
xo = arma_generate_sample([1, -0.8, 0.5], [1], nsample=nsample)
data.xar2 = np.round(xo*1000).astype(int)
np.random.seed(10000)
xo = np.random.normal(size=nsample)
data.xnormal = np.round(xo*1000).astype(int)
import matplotlib.mlab as mlab
from gwstatsmodels.tsa.arima_process import arma_generate_sample, arma_impulse_response
from gwstatsmodels.tsa.arima_process import arma_acovf, arma_acf, ARIMA
#from movstat import acf, acovf
#from gwstatsmodels.sandbox.tsa import acf, acovf, pacf
from gwstatsmodels.tsa.stattools import acf, acovf, pacf
ar = [1., -0.6]
#ar = [1., 0.]
ma = [1., 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)
import scikits.talkbox as stb
import scikits.talkbox.spectral.basic as stbs
except:
hastalkbox = False
ar = [1., -0.7] #[1,0,0,0,0,0,0,-0.7]
ma = [1., 0.3]
ar = np.convolve([1.] + [0] * 50 + [-0.6], ar)
ar = np.convolve([1., -0.5] + [0] * 49 + [-0.3], ar)
n_startup = 1000
nobs = 1000
# throwing away samples at beginning makes sample more "stationary"
xo = arma_generate_sample(ar, ma, n_startup + nobs)
x = xo[n_startup:]
#moved to tsa.arima_process
#def arma_periodogram(ar, ma, **kwds):
# '''periodogram for ARMA process given by lag-polynomials ar and ma
#
# Parameters
# ----------
# ar : array_like
# autoregressive lag-polynomial with leading 1 and lhs sign
# ma : array_like
# moving average lag-polynomial with leading 1
# kwds : options
# options for scipy.signal.freqz
# default: worN=None, whole=0
_wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(
tsbase.TimeSeriesResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(ARMAResultsWrapper, ARMAResults)
if __name__ == "__main__":
import numpy as np
import gwstatsmodels.api as sm
# simulate arma process
from gwstatsmodels.tsa.arima_process import arma_generate_sample
y = arma_generate_sample([1., -.75], [1., .25], nsample=1000)
arma = ARMA(y)
res = arma.fit(trend='nc', order=(1, 1))
np.random.seed(12345)
y_arma22 = arma_generate_sample([1., -.85, .35], [1, .25, -.9],
nsample=1000)
arma22 = ARMA(y_arma22)
res22 = arma22.fit(trend='nc', order=(2, 2))
# test CSS
arma22_css = ARMA(y_arma22)
res22css = arma22_css.fit(trend='nc', order=(2, 2), method='css')
data = sm.datasets.sunspots.load()
ar = ARMA(data.endog)
if not wasinvertible:
start_params = res.params.copy()
start_params[self.nar: self.nar+self.nma] = mainv[1:]
#need to add args kwds
res = self.fit(start_params=start_params)
return res
if __name__ == '__main__':
nobs = 50
ar = [1.0, -0.8, 0.1]
ma = [1.0, 0.1, 0.2]
#ma = [1]
np.random.seed(9875789)
y = arma_generate_sample(ar,ma,nobs,2)
y -= y.mean() #I haven't checked treatment of mean yet, so remove
mod = MLEGLS(y)
mod.nar, mod.nma = 2, 2 #needs to be added, no init method
mod.nobs = len(y)
res = mod.fit(start_params=[0.1, -0.8, 0.2, 0.1, 1.])
print 'DGP', ar, ma
print res.params
from gwstatsmodels.regression import yule_walker
print yule_walker(y, 2)
#resi = mod.fit_invertible(start_params=[0.1,0,0.2,0, 0.5])
#print resi.params
arpoly, mapoly = getpoly(mod, res.params[:-1])
data = sm.datasets.sunspots.load()
_attrs = {}
_wrap_attrs = wrap.union_dicts(tsbase.TimeSeriesResultsWrapper._wrap_attrs,
_attrs)
_methods = {}
_wrap_methods = wrap.union_dicts(
tsbase.TimeSeriesResultsWrapper._wrap_methods,
_methods)
wrap.populate_wrapper(ARMAResultsWrapper, ARMAResults)
if __name__ == "__main__":
import numpy as np
import gwstatsmodels.api as sm
# simulate arma process
from gwstatsmodels.tsa.arima_process import arma_generate_sample
y = arma_generate_sample([1., -.75],[1.,.25], nsample=1000)
arma = ARMA(y)
res = arma.fit(trend='nc', order=(1,1))
np.random.seed(12345)
y_arma22 = arma_generate_sample([1.,-.85,.35],[1,.25,-.9], nsample=1000)
arma22 = ARMA(y_arma22)
res22 = arma22.fit(trend = 'nc', order=(2,2))
# test CSS
arma22_css = ARMA(y_arma22)
res22css = arma22_css.fit(trend='nc', order=(2,2), method='css')
data = sm.datasets.sunspots.load()
ar = ARMA(data.endog)
import numpy as np
from gwstatsmodels.tsa.arima_process import arma_generate_sample
from gwstatsmodels.iolib import savetxt
np.random.seed(12345)
# no constant
y_arma11 = arma_generate_sample([1., -.75],[1., .35], nsample=250)
y_arma14 = arma_generate_sample([1., -.75],[1., .35, -.75, .1, .35],
nsample=250)
y_arma41 = arma_generate_sample([1., -.75, .25, .25, -.75], [1., .35],
nsample=250)
y_arma22 = arma_generate_sample([1., -.75, .45],[1., .35, -.9], nsample=250)
y_arma50 = arma_generate_sample([1., -.75, .35, -.3, -.2, .1], [1.],
nsample=250)
y_arma02 = arma_generate_sample([1.], [1., .35, -.75], nsample=250)
# constant
constant = 4.5
y_arma11c = arma_generate_sample([1., -.75],[1., .35], nsample=250) + constant
y_arma14c = arma_generate_sample([1., -.75],[1., .35, -.75, .1, .35],
nsample=250) + constant
y_arma41c = arma_generate_sample([1., -.75, .25, .25, -.75], [1., .35],
nsample=250) + constant
y_arma22c = arma_generate_sample([1., -.75, .45],[1., .35, -.9], nsample=250) + \
constant
y_arma50c = arma_generate_sample([1., -.75, .35, -.3, -.2, .1], [1.],
