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])
plt.plot(wm,sdm/sdm[0], '-', wm[maxind], sdm[maxind]/sdm[0], 'o')
else:
plt.plot(wm, sdm, '-', wm[maxind], sdm[maxind], 'o')
plt.title('matplotlib')
if hastalkbox:
sdp, wp = stbs.periodogram(x)
plt.subplot(2,3,3)
if rescale:
plt.plot(wp,sdp/sdp[0])
else:
plt.plot(wp, sdp)
plt.title('stbs.periodogram')
xacov = acovf(x, unbiased=False)
plt.subplot(2,3,4)
plt.plot(xacov)
plt.title('autocovariance')
nr = len(x)#*2/3
#xacovfft = np.fft.fft(xacov[:nr], 2*nr-1)
xacovfft = np.fft.fft(np.correlate(x,x,'full'))
#abs(xacovfft)**2 or equivalently
xacovfft = xacovfft * xacovfft.conj()
plt.subplot(2,3,5)
if rescale:
plt.plot(xacovfft[:nr]/xacovfft[0])
else:
plt.plot(xacovfft[:nr])
arrvs = ar_generator() ##arma = ARIMA() ##res = arma.fit(arrvs[0], 4, 0) arma = ARIMA(arrvs[0]) res = arma.fit((4,0, 0)) print res[0] acf1 = acf(arrvs[0]) acovf1b = acovf(arrvs[0], unbiased=False) acf2 = autocorr(arrvs[0]) acf2m = autocorr(arrvs[0]-arrvs[0].mean()) print acf1[:10] print acovf1b[:10] print acf2[:10] print acf2m[:10] x = arma_generate_sample([1.0, -0.8], [1.0], 500) print acf(x)[:20] import gwstatsmodels.api as sm print sm.regression.yule_walker(x, 10) import matplotlib.pyplot as plt #ax = plt.axes()
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])
plt.plot(wm, sdm / sdm[0], '-', wm[maxind], sdm[maxind] / sdm[0], 'o')
else:
plt.plot(wm, sdm, '-', wm[maxind], sdm[maxind], 'o')
plt.title('matplotlib')
if hastalkbox:
sdp, wp = stbs.periodogram(x)
plt.subplot(2, 3, 3)
if rescale:
plt.plot(wp, sdp / sdp[0])
else:
plt.plot(wp, sdp)
plt.title('stbs.periodogram')
xacov = acovf(x, unbiased=False)
plt.subplot(2, 3, 4)
plt.plot(xacov)
plt.title('autocovariance')
nr = len(x) #*2/3
#xacovfft = np.fft.fft(xacov[:nr], 2*nr-1)
xacovfft = np.fft.fft(np.correlate(x, x, 'full'))
#abs(xacovfft)**2 or equivalently
xacovfft = xacovfft * xacovfft.conj()
plt.subplot(2, 3, 5)
if rescale:
plt.plot(xacovfft[:nr] / xacovfft[0])
else:
plt.plot(xacovfft[:nr])