def test_no_change_lags_trend(self):
adf = ADF(self.inflation)
lags = adf.lags
with pytest.warns(FutureWarning, match="Mutating unit root"):
adf.lags = lags
trend = adf.trend
with pytest.warns(FutureWarning, match="Mutating unit root"):
adf.trend = trend
ml = adf.max_lags
with pytest.warns(FutureWarning, match="Mutating unit root"):
adf.max_lags = ml
def ADF_test(self, df_ts, lags=None):
if lags == 'None':
try:
adf = ADF(df_ts)
except:
adf = ADF(df_ts.dropna())
else:
try:
adf = ADF(df_ts)
except:
adf = ADF(df_ts.dropna())
adf.lags = lags
print(adf.summary().as_text())
return adf
def ADF_test(df_ts, lags=None):
"""
ADF from arch
formula:
xt-xt-1 ~ b0 + (b1-1)*xt-1 + e
test if b1-1 == 0 ~ DF statistics
:param df_ts:
:param lags:
:return:
"""
if lags == 'None':
try:
adf = ADF(df_ts)
except:
adf = ADF(df_ts.dropna())
else:
try:
adf = ADF(df_ts)
except:
adf = ADF(df_ts.dropna())
adf.lags = lags
print(adf.summary().as_text())
return adf
def test_negative_lag(self):
adf = ADF(self.inflation)
with pytest.raises(ValueError):
adf.lags = -1
def test_negative_lag(self):
adf = ADF(self.inflation)
with pytest.raises(ValueError):
adf.lags = -1
from statsmodels.tsa.arima_model import ARMA
from sklearn.metrics import mean_squared_error as mse
from sklearn.metrics import r2_score as r2
from arch.unitroot import ADF
def parser(x):
return datetime.strptime(x,'%d/%m/%Y')
data = pd.read_csv('Bitcoin-Train.csv',index_col=0,parse_dates= [0],date_parser = parser)
test_data = pd.read_csv('Bitcoin-Test.csv',index_col=0,parse_dates = [0],date_parser = parser)
# Augmented Dickey Fuller Test for Stationarity
adf_test = ADF(data,regression = 'c')
adf_test.lags = 2
adf.trend = 'c'
print(adf_test.summary().as_text())
plt.plot(data)
# ACF and PACF of Raw data
plot_acf(data)
plot_pacf(data)
### Stationarity Conversion ###
data_diff = data.diff(periods = 1)
data_diff = data_diff[1:]
### ACF and PACF for differenced Time Series ###
