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 numdifftools as ndt
import scikits.statsmodels.api as sm
from scikits.statsmodels.sandbox import tsa
from scikits.statsmodels.tsa.arma_mle import Arma # local import
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
examples = ['arma']
if 'arma' in examples:
print "\nExample 1"
print '----------'
ar = [1.0, -0.8]
ma = [1.0, 0.5]
y1 = arma_generate_sample(ar,ma,1000,0.1)
y1 -= y1.mean() #no mean correction/constant in estimation so far
arma1 = Arma(y1)
arma1.nar = 1
arma1.nma = 1
arma1res = arma1.fit_mle(order=(1,1), method='fmin')
print arma1res.params
#Warning need new instance otherwise results carry over
arma2 = Arma(y1)
arma2.nar = 1
arma2.nma = 1
res2 = arma2.fit(method='bfgs')
print res2.params
print res2.model.hessian(res2.params)
@cache_readonly
def pvalues(self):
# TODO: same for conditional and unconditional?
df_resid = self.df_resid
return t.sf(np.abs(self.tvalues), df_resid) * 2
if __name__ == "__main__":
import numpy as np
import scikits.statsmodels.api as sm
# simulate arma process
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
y = arma_generate_sample([1.0, -0.75], [1.0, 0.25], nsample=1000)
arma = ARMA(y)
res = arma.fit(trend="nc", order=(1, 1))
np.random.seed(12345)
y_arma22 = arma_generate_sample([1.0, -0.85, 0.35], [1, 0.25, -0.9], 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)
resar = ar.fit(trend="nc", order=(9, 0))
rescmt = modct.fit_mle(order=(1, 1), start_params=[-0.4, 0.4, 10, 1.0], maxiter=500, maxfun=500) print rescmt.params from scikits.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 scikits.statsmodels.tsa.arima_process import arma_generate_sample np.random.seed(12345) y_arma22 = arma_generate_sample([1.0, -0.85, 0.35, -0.1], [1, 0.25, -0.7], nsample=1000) ##arma22 = ARMA(y_arma22) ##res22 = arma22.fit(trend = 'nc', order=(2,2)) ##print 'kf ',res22.params ##res22css = arma22.fit(method='css',trend = 'nc', order=(2,2)) ##print 'css', res22css.params mod22 = Arma(y_arma22) resls22 = mod22.fit(order=(2, 2)) print "ls ", resls22[0] resmle22 = mod22.fit_mle(order=(2, 2), maxfun=2000) print "mle", resmle22.params f = mod22.forecast() f3 = mod22.forecast3(start=900)[-20:] print y_arma22[-10:]
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
_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 scikits.statsmodels.api as sm
# simulate arma process
from scikits.statsmodels.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 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
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 scikits.statsmodels.tsa.arima import ARMA
mkf = ARMA(x)
##rkf = mkf.fit((1,1))
##rkf.params
rkf = mkf.fit((1,1), trend='nc')
print rkf.params
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
np.random.seed(12345)
y_arma22 = arma_generate_sample([1.,-.85,.35, -0.1],[1,.25,-.7], nsample=1000)
##arma22 = ARMA(y_arma22)
##res22 = arma22.fit(trend = 'nc', order=(2,2))
##print 'kf ',res22.params
##res22css = arma22.fit(method='css',trend = 'nc', order=(2,2))
##print 'css', res22css.params
mod22 = Arma(y_arma22)
resls22 = mod22.fit(order=(2,2))
print 'ls ', resls22[0]
resmle22 = mod22.fit_mle(order=(2,2), maxfun=2000)
print 'mle', resmle22.params
f = mod22.forecast()
f3 = mod22.forecast3(start=900)[-20:]
print y[-10:]
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 scikits.statsmodels.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()
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 scikits.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 scikits.statsmodels.tsa.arima_process import arma_generate_sample
np.random.seed(12345)
y_arma22 = arma_generate_sample([1.,-.85,.35, -0.1],[1,.25,-.7], nsample=1000)
##arma22 = ARMA(y_arma22)
##res22 = arma22.fit(trend = 'nc', order=(2,2))
##print 'kf ',res22.params
##res22css = arma22.fit(method='css',trend = 'nc', order=(2,2))
##print 'css', res22css.params
mod22 = Arma(y_arma22)
resls22 = mod22.fit(order=(2,2))
print 'ls ', resls22[0]
resmle22 = mod22.fit_mle(order=(2,2), maxfun=2000)
print 'mle', resmle22.params
f = mod22.forecast()
f3 = mod22.forecast3(start=900)[-20:]
print y_arma22[-10:]
import numpy as np
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
from scikits.statsmodels.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.],
'''generates some ARMA random samples and saves to python module file
'''
import numpy as np
from scikits.statsmodels.sandbox import tsa
from scikits.statsmodels.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 scikits.statsmodels.tsa.arima_process import arma_generate_sample, arma_impulse_response
from scikits.statsmodels.tsa.arima_process import arma_acovf, arma_acf, ARIMA
#from movstat import acf, acovf
#from scikits.statsmodels.sandbox.tsa import acf, acovf, pacf
from scikits.statsmodels.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':
_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 scikits.statsmodels.api as sm
# simulate arma process
from scikits.statsmodels.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 scikits.statsmodels.sandbox import tsa
from scikits.statsmodels.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 scikits.statsmodels.tsa.arima_process import arma_generate_sample, arma_impulse_response
from scikits.statsmodels.tsa.arima_process import arma_acovf, arma_acf, ARIMA
# from movstat import acf, acovf
# from scikits.statsmodels.sandbox.tsa import acf, acovf, pacf
from scikits.statsmodels.tsa.stattools 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)
import numdifftools as ndt
import scikits.statsmodels.api as sm
from scikits.statsmodels.sandbox import tsa
from scikits.statsmodels.tsa.arma_mle import Arma # local import
from scikits.statsmodels.tsa.arima_process import arma_generate_sample
examples = ['arma']
if 'arma' in examples:
print "\nExample 1"
print '----------'
ar = [1.0, -0.8]
ma = [1.0, 0.5]
y1 = arma_generate_sample(ar, ma, 1000, 0.1)
y1 -= y1.mean() #no mean correction/constant in estimation so far
arma1 = Arma(y1)
arma1.nar = 1
arma1.nma = 1
arma1res = arma1.fit_mle(order=(1, 1), method='fmin')
print arma1res.params
#Warning need new instance otherwise results carry over
arma2 = Arma(y1)
arma2.nar = 1
arma2.nma = 1
res2 = arma2.fit(method='bfgs')
print res2.params
print res2.model.hessian(res2.params)
def _generate_ar_return(iterations, ar, phi):
arima_process.arma_generate_sample([ar], [phi], iterations, sigma=1)
"""
Example: scikits.statsmodels.tsa.ARMA
"""
import numpy as np
import scikits.statsmodels.api as sm
# Generate some data from an ARMA process
from scikits.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)
#Example TSA ARMA
import numpy as np
import scikits.statsmodels.api as sm
# Generate some data from an ARMA process
from scikits.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)
plt.figure()
plt.plot(y)
#Now, optionally, we can add some dates information. For this example,
# we'll use a pandas time series.
dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)
y = Series(y, index=dates)
arma_mod = sm.tsa.ARMA(y, freq='M')
#arma_res = arma_mod.fit(order=(2,2), trend='nc', disp=-1) #fails
#old pandas 0.4.0: AttributeError: 'TimeSeries' object has no attribute 'name'
#arma_res.params
plt.show()
mainv, wasinvertible = invertibleroots(ma)
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 scikits.statsmodels.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()
import numpy as np import scikits.statsmodels.api as sm from scikits.statsmodels.tsa.arima_process import arma_generate_sample from scikits.statsmodels.tsa.arma_mle import Arma as Arma from scikits.statsmodels.tsa.arima_process import ARIMA as ARIMA_old from scikits.statsmodels.sandbox.tsa.garch import Arma as Armamle_old from scikits.statsmodels.tsa.arima import ARMA as ARMA_kf print "\nExample 1" ar = [1.0, -0.6, 0.1] ma = [1.0, 0.5, 0.3] nobs = 1000 y22 = arma_generate_sample(ar, ma, nobs+1000, 0.5)[-nobs:] y22 -= y22.mean() start_params = [0.1, 0.1, 0.1, 0.1] start_params_lhs = [-0.1, -0.1, 0.1, 0.1] print 'truelhs', np.r_[ar[1:], ma[1:]] ###bug in current version, fixed in Skipper and 1 more ###arr[1:q,:] = params[p+k:p+k+q] # p to p+q short params are MA coeffs ###ValueError: array dimensions are not compatible for copy ##arma22 = ARMA_kf(y22, constant=False, order=(2,2)) ##res = arma22.fit(start_params=start_params)
