def __init__(self):
self.acf = self.results['acvarfft']
self.qstat = self.results['Q1']
self.res1 = acf(self.x, nlags=40, qstat=True, fft=True)
xhat5, err5 = VARMA(x, B, C)
#print err5
#in differences
#VARMA(np.diff(x,axis=0),B,C)
#Note:
# * signal correlate applies same filter to all columns if kernel.shape[1]<K
# e.g. signal.correlate(x0,np.ones((3,1)),'valid')
# * if kernel.shape[1]==K, then `valid` produces a single column
# -> possible to run signal.correlate K times with different filters,
# see the following example, which replicates VAR filter
x0 = np.column_stack([np.arange(T), 2 * np.arange(T)])
B[:, :, 0] = np.ones((P, K))
B[:, :, 1] = np.ones((P, K))
B[1, 1, 1] = 0
xhat0 = VAR(x0, B)
xcorr00 = signal.correlate(x0, B[:, :, 0]) #[:,0]
xcorr01 = signal.correlate(x0, B[:, :, 1])
print np.all(
signal.correlate(x0, B[:, :, 0], 'valid')[:-1, 0] == xhat0[P:, 0])
print np.all(
signal.correlate(x0, B[:, :, 1], 'valid')[:-1, 0] == xhat0[P:, 1])
#import error
#from movstat import acovf, acf
from gwstatsmodels.tsa.stattools import acovf, acf
aav = acovf(x[:, 0])
print aav[0] == np.var(x[:, 0])
aac = acf(x[:, 0])
xhat5, err5 = VARMA(x,B,C)
#print err5
#in differences
#VARMA(np.diff(x,axis=0),B,C)
#Note:
# * signal correlate applies same filter to all columns if kernel.shape[1]<K
# e.g. signal.correlate(x0,np.ones((3,1)),'valid')
# * if kernel.shape[1]==K, then `valid` produces a single column
# -> possible to run signal.correlate K times with different filters,
# see the following example, which replicates VAR filter
x0 = np.column_stack([np.arange(T), 2*np.arange(T)])
B[:,:,0] = np.ones((P,K))
B[:,:,1] = np.ones((P,K))
B[1,1,1] = 0
xhat0 = VAR(x0,B)
xcorr00 = signal.correlate(x0,B[:,:,0])#[:,0]
xcorr01 = signal.correlate(x0,B[:,:,1])
print np.all(signal.correlate(x0,B[:,:,0],'valid')[:-1,0]==xhat0[P:,0])
print np.all(signal.correlate(x0,B[:,:,1],'valid')[:-1,0]==xhat0[P:,1])
#import error
#from movstat import acovf, acf
from gwstatsmodels.tsa.stattools import acovf, acf
aav = acovf(x[:,0])
print aav[0] == np.var(x[:,0])
aac = acf(x[:,0])
def __init__(self):
self.acf = self.results['acvar']
#self.acf = np.concatenate(([1.], self.acf))
self.qstat = self.results['Q1']
self.res1 = acf(self.x, nlags=40, qstat=True)
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)
elif key=='linear':
def __init__(self):
self.acf = self.results['acvarfft']
self.qstat = self.results['Q1']
self.res1 = acf(self.x, nlags=40, qstat=True, fft=True)
def __init__(self):
self.acf = self.results['acvar']
#self.acf = np.concatenate(([1.], self.acf))
self.qstat = self.results['Q1']
self.res1 = acf(self.x, nlags=40, qstat=True)
def test_acf():
acf_x = tsa.acf(x100, unbiased=False)[:21]
assert_array_almost_equal(mlacf.acf100.ravel(), acf_x, 8) #why only dec=8
acf_x = tsa.acf(x1000, unbiased=False)[:21]
assert_array_almost_equal(mlacf.acf1000.ravel(), acf_x, 8) #why only dec=9
def test_acf():
acf_x = tsa.acf(x100, unbiased=False)[:21]
assert_array_almost_equal(mlacf.acf100.ravel(), acf_x, 8) #why only dec=8
acf_x = tsa.acf(x1000, unbiased=False)[:21]
assert_array_almost_equal(mlacf.acf1000.ravel(), acf_x, 8) #why only dec=9
