def test_plotacf():
# Just test that it runs.
fig = plt.figure()
ax = fig.add_subplot(111)
ar = np.r_[1., -0.9]
ma = np.r_[1., 0.9]
armaprocess = tsp.ArmaProcess(ar, ma)
acf = armaprocess.acf(20)[:20]
plotacf(acf, ax=ax)
plt.close(fig)
def test_plotacf():
# Just test that it runs.
fig = plt.figure()
ax = fig.add_subplot(111)
ar = np.r_[1., -0.9]
ma = np.r_[1., 0.9]
armaprocess = tsp.ArmaProcess(ar, ma)
acf = armaprocess.acf(20)[:20]
plotacf(acf, ax=ax)
plt.close(fig)
subplotcount = 1
nrows = 4
for arcoef in arcoefs[:-1]:
for macoef in macoefs[:-1]:
ar = np.r_[1., -arcoef]
ma = np.r_[1., macoef]
#from statsmodels.sandbox.tsa.fftarma import ArmaFft as FftArmaProcess
#y = tsp.arma_generate_sample(ar,ma,nsample, sig, burnin)
#armaprocess = FftArmaProcess(ar, ma, nsample) #TODO: make n optional
#armaprocess.plot4()
armaprocess = tsp.ArmaProcess(ar, ma)
acf = armaprocess.acf(20)[:20]
pacf = armaprocess.pacf(20)[:20]
ax = fig.add_subplot(nrows, 2, subplotcount)
plotacf(acf, ax=ax)
## ax.set_title('Autocorrelation \nar=%s, ma=%rs' % (ar, ma),
## size='xx-small')
ax.text(
0.7,
0.6,
'ar =%s \nma=%s' % (ar, ma),
transform=ax.transAxes,
horizontalalignment='left', #'right',
size='xx-small')
ax.set_xlim(-1, 20)
subplotcount += 1
ax = fig.add_subplot(nrows, 2, subplotcount)
plotacf(pacf, ax=ax)
## ax.set_title('Partial Autocorrelation \nar=%s, ma=%rs' % (ar, ma),
## size='xx-small')
fig.suptitle('ARMA: Autocorrelation (left) and Partial Autocorrelation (right)')
subplotcount = 1
nrows = 4
for arcoef in arcoefs[:-1]:
for macoef in macoefs[:-1]:
ar = np.r_[1., -arcoef]
ma = np.r_[1., macoef]
#y = tsp.arma_generate_sample(ar,ma,nsample, sig, burnin)
#armaprocess = FftArmaProcess(ar, ma, nsample) #TODO: make n optional
#armaprocess.plot4()
armaprocess = tsp.ArmaProcess(ar, ma)
acf = armaprocess.acf(20)[:20]
pacf = armaprocess.pacf(20)[:20]
ax = fig.add_subplot(nrows, 2, subplotcount)
plotacf(acf, ax=ax)
## ax.set_title('Autocorrelation \nar=%s, ma=%rs' % (ar, ma),
## size='xx-small')
ax.text(0.7, 0.6, 'ar =%s \nma=%s' % (ar, ma),
transform=ax.transAxes,
horizontalalignment='left', #'right',
size='xx-small')
ax.set_xlim(-1,20)
subplotcount +=1
ax = fig.add_subplot(nrows, 2, subplotcount)
plotacf(pacf, ax=ax)
## ax.set_title('Partial Autocorrelation \nar=%s, ma=%rs' % (ar, ma),
## size='xx-small')
ax.text(0.7, 0.6, 'ar =%s \nma=%s' % (ar, ma),
transform=ax.transAxes,
horizontalalignment='left', #'right',
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 statsmodels.api as sm print(sm.regression.yule_walker(x, 10)) import matplotlib.pyplot as plt #ax = plt.axes() plt.plot(x) #plt.show() plt.figure() pltxcorr(plt,x,x) plt.figure() pltxcorr(plt,x,x, usevlines=False) plt.figure() #FIXME: plotacf was moved to graphics/tsaplots.py, and interface changed plotacf(plt, acf1[:20], np.arange(len(acf1[:20])), usevlines=True) plt.figure() ax = plt.subplot(211) plotacf(ax, acf1[:20], usevlines=True) ax = plt.subplot(212) plotacf(ax, acf1[:20], np.arange(len(acf1[:20])), usevlines=False) #plt.show()
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]) print(sm.regression.yule_walker(x, 10)) #ax = plt.axes() plt.plot(x) #plt.show() plt.figure() pltxcorr(plt, x, x) plt.figure() pltxcorr(plt, x, x, usevlines=False) plt.figure() #FIXME: plotacf was moved to graphics/tsaplots.py, and interface changed plotacf(plt, acf1[:20], np.arange(len(acf1[:20])), usevlines=True) plt.figure() ax = plt.subplot(211) plotacf(ax, acf1[:20], usevlines=True) ax = plt.subplot(212) plotacf(ax, acf1[:20], np.arange(len(acf1[:20])), usevlines=False) #plt.show()
