def test_rgnpq_case(self):
mdlfile = os.path.join(self.run_dir, 'rgnpq.csv')
mdl = np.asarray(pd.read_csv(mdlfile))
res = zivot_andrews(mdl, maxlag=12, regression='t', autolag='t-stat')
assert_allclose([res[0], res[1], res[3], res[4]],
[-3.02761, 0.63993, 12, 102],
rtol=1e-3)
def test_rand10000_case(self):
mdlfile = os.path.join(self.run_dir, 'rand10000.csv')
mdl = np.asarray(pd.read_csv(mdlfile))
res = zivot_andrews(mdl, regression='c', autolag='t-stat')
assert_allclose([res[0], res[1], res[3], res[4]],
[-3.48223, 0.69111, 25, 7071],
rtol=1e-3)
def test_stkprc_case(self):
mdlfile = os.path.join(self.run_dir, 'stkprc.csv')
mdl = np.asarray(pd.read_csv(mdlfile))
res = zivot_andrews(mdl, maxlag=8, regression='ct', autolag='t-stat')
assert_allclose([res[0], res[1], res[3], res[4]],
[-5.60689, 0.00894, 1, 65],
rtol=1e-3)
def test_gnpdef_case(self):
mdlfile = os.path.join(self.run_dir, 'gnpdef.csv')
mdl = np.asarray(pd.read_csv(mdlfile))
res = zivot_andrews(mdl, maxlag=8, regression='c', autolag='t-stat')
assert_allclose([res[0], res[1], res[3], res[4]],
[-4.12155, 0.28024, 5, 40],
rtol=1e-3)
def test_rgnp_case(self):
res = zivot_andrews(self.fail_mdl,
maxlag=8,
regression='c',
autolag=None)
assert_allclose([res[0], res[1], res[4]], [-5.57615, 0.00312, 20],
rtol=1e-3)
def check_stationarity(self, df):
# Initializing flags.
flag_news = False
flag_tweets = False
# Getting column list.
col_list = df.columns.tolist()
# Checking for tweets.
result_tweets = zivot_andrews(df[col_list[0]])
# Checking for news.
result_news = adfuller(df[col_list[1]])
# Displaying results.
print("Result for news:")
print("P-value: ", result_news[1])
if (result_news[1] <= 0.05):
flag_news = True
print("Stationary: True")
else:
print("Stationary: False")
print("Result for tweets:")
print("P-value: ", result_tweets[1])
if (result_tweets[1] <= 0.05):
flag_tweets = True
print("Stationary: True")
else:
print("Stationary: False")
print("--------------------------------------")
return flag_news, flag_tweets
def zivot_andrews_test(timeseries):
print('Results of Zivot-Andrews Test:')
zatest = zivot_andrews(timeseries, regression='ct', autolag='AIC')
zaoutput = pd.Series(zatest[0:5],
index=[
'Test Statistic', 'p-value', 'Critical Values',
'Lags Used', 'Break Time'
])
print(zaoutput)
def test_autolag_case_sensitivity(self, autolag):
res = zivot_andrews(self.fail_mdl, autolag=autolag)
assert res[3] == 1
def test_fail_autolag_type(self):
with pytest.raises(ValueError):
zivot_andrews(self.fail_mdl, autolag='None')
def test_fail_array_shape(self):
with pytest.raises(ValueError):
zivot_andrews(np.random.rand(50, 2))
def test_fail_trim_value(self):
with pytest.raises(ValueError):
zivot_andrews(self.fail_mdl, trim=0.5)
def test_fail_regression_type(self):
with pytest.raises(ValueError):
zivot_andrews(self.fail_mdl, regression='x')
