def test_ld(self):
pacfyw = pacf_yw(self.x, nlags=40, method="mle")
pacfld = pacf(self.x, nlags=40, method="ldb")
assert_almost_equal(pacfyw, pacfld, DECIMAL_8)
pacfyw = pacf(self.x, nlags=40, method="yw")
pacfld = pacf(self.x, nlags=40, method="ldu")
assert_almost_equal(pacfyw, pacfld, DECIMAL_8)
def test_ld(self):
pacfyw = pacf_yw(self.x, nlags=40, method="mle")
pacfld = pacf(self.x, nlags=40, method="ldb")
assert_almost_equal(pacfyw, pacfld, DECIMAL_8)
pacfyw = pacf(self.x, nlags=40, method="yw")
pacfld = pacf(self.x, nlags=40, method="ldu")
assert_almost_equal(pacfyw, pacfld, DECIMAL_8)
def d_param(self, diff):
'''function takes different values for difference step, and returns true or false flag if acf and pacf values
lie into the threshold area'''
THRESHOLD = 0.08
if diff == 0:
acf = tss.acf(self.val)
pacf = tss.pacf(self.val)
# acf and pacf plots
fig = plt.figure(figsize = (12,8))
ax1 = fig.add_subplot(121)
fig = plot_acf(self.val,lags =40 ,ax=ax1)
ax2 = fig.add_subplot(122, sharey=ax1)
fig= plot_pacf(self.val, lags = 40, ax =ax2)
plt.savefig('ACF_vs_PACF.jpg')
plt.close()
# check if most acf and pacf are lie in the accepted region for diff0
acf_percent = len(acf[np.abs(acf) <= THRESHOLD])/float(len(acf))
pacf_percent = len(pacf[np.abs(pacf) <= THRESHOLD])/float(len(pacf))
return (acf_percent >= .65) and (pacf_percent >= 0.65)
elif diff == 1:
diff1_acf = tss.acf(self.diff1_val.dropna())
diff1_pacf = tss.pacf(self.diff1_val.dropna())
# for acf and pacf plots
fig = plt.figure(figsize = (12,8))
ax1 = fig.add_subplot(121)
fig = plot_acf(self.diff1_val.dropna(),lags =40 ,ax=ax1)
ax2 = fig.add_subplot(122, sharey=ax1)
fig= plot_pacf(self.diff1_val.dropna(), lags = 40, ax =ax2)
plt.savefig('ACF_vs_PACF_diff1.jpg')
plt.close()
# check if most acf and pacf are lie in the accepted region for diff1
acf_percent = len(diff1_acf[np.abs(diff1_acf) <= THRESHOLD])/float(len(diff1_acf))
pacf_percent = len(diff1_pacf[np.abs(diff1_pacf) <= THRESHOLD])/float(len(diff1_pacf))
return (acf_percent >= .65) and (pacf_percent >= 0.65)
elif diff == 2:
diff2_acf = tss.acf(self.diff2_val.dropna())
diff2_pacf = tss.pacf(self.diff2_val.dropna())
# check save fig for acf and pacf plots
fig = plt.figure(figsize = (12,8))
ax1 = fig.add_subplot(121)
fig = plot_acf(self.diff2_val.dropna(),lags =40 ,ax=ax1)
ax2 = fig.add_subplot(122, sharey=ax1)
fig = plot_pacf(self.diff2_val.dropna(), lags = 40, ax =ax2)
plt.savefig('ACF_vs_PACF_diff2.jpg')
plt.close()
# check if most acf and pacf are lie in the accepted region for diff2
acf_percent = len(diff2_acf[np.abs(diff2_acf) <= THRESHOLD])/float(len(diff2_acf))
pacf_percent = len(diff2_pacf[np.abs(diff2_pacf) <= THRESHOLD])/float(len(diff2_pacf))
return (acf_percent >= .65) and (pacf_percent >= 0.65)
else:
raise InvalidParamError
def plot_acf(y, lags=100, partial=False, ax=None):
from scikits.statsmodels.tsa.stattools import acf, pacf
if partial:
the_acf = pacf(y, nlags=lags)
else:
the_acf = acf(y, nlags=lags)
if ax is None:
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(111)
ax.vlines(np.arange(lags + 1), [0], the_acf)
ax.axhline(0, color='k')
def plot_acf(y, lags=100, partial=False, ax=None):
from scikits.statsmodels.tsa.stattools import acf, pacf
if partial:
the_acf = pacf(y, nlags=lags)
else:
the_acf = acf(y, nlags=lags)
if ax is None:
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(111)
ax.vlines(np.arange(lags+1), [0], the_acf)
ax.axhline(0, color='k')
