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I added below : import statsmodels from statsmodels.tsa.stattools import adfuller in first input.
Then I tested the code till : #Stationarity test def test_stationarity(timeseries): ... ... I got stuck with this error message :
#Stationarity test
def test_stationarity(timeseries):
...
Results of dickey fuller test
--------------------------------------------------------------------------- MissingDataError Traceback (most recent call last) Input In [102], in <cell line: 27>() 25 else: 26 print("Weak evidence against null hypothesis, time series is non-stationary ") ---> 27 test_stationarity(train['Close']) `Input In [102], in test_stationarity(timeseries)` ` 16 plt.show(block = False)` ` 18 print('Results of dickey fuller test')` `---> 19 result = adfuller(timeseries, autolag = 'AIC')` ` 20 labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']` ` 21 for value,label in zip(result, labels):` File ~/.local/lib/python3.10/site-packages/statsmodels/tsa/stattools.py:321, in adfuller(x, maxlag, regression, autolag, store,regresults) 315 # 1 for level 316 # search for lag length with smallest information criteria 317 # Note: use the same number of observations to have comparable IC 318 # aic and bic: smaller is better 320 if not regresults: --> 321 icbest, bestlag = _autolag( 322 OLS, xdshort, fullRHS, startlag, maxlag, autolag 323 ) 324 else: 325 icbest, bestlag, alres = _autolag( 326 OLS, 327 xdshort, (...) 332 regresults=regresults, 333 ) `File ~/.local/lib/python3.10/site-packages/statsmodels/tsa/stattools.py:129, in _autolag(mod, endog, exog, startlag, maxlag,method, modargs, fitargs, regresults) 127 method = method.lower() 128 for lag in range(startlag, startlag + maxlag + 1): --> 129 mod_instance = mod(endog, exog[:, :lag], *modargs) 130 results[lag] = mod_instance.fit() 132 if method == "aic":``File ~/.local/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:906, in OLS.init(self, endog, exog, missing, hasconst, **kwargs)` ` 903 msg = ("Weights are not supported in OLS and will be ignored"` ` 904 "An exception will be raised in the next version.")` ` 905 warnings.warn(msg, ValueWarning)` `--> 906 super(OLS, self).__init__(endog, exog, missing=missing,` ` 907 hasconst=hasconst, **kwargs)` ` 908 if "weights" in self._init_keys:` ` 909 self._init_keys.remove("weights")` File ~/.local/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:733, in WLS.__init__(self, endog, exog, ``weights, missing, hasconst, **kwargs) 731 else: 732 weights = weights.squeeze() --> 733 super(WLS, self).__init__(endog, exog, missing=missing, 734 weights=weights, hasconst=hasconst, **kwargs) 735 nobs = self.exog.shape[0] 736 weights = self.weights `File ~/.local/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:190, in RegressionModel.__init__(self, endog,exog, **kwargs) 189 def init(self, endog, exog, **kwargs): --> 190 super(RegressionModel, self).init(endog, exog, **kwargs) 191 self._data_attr.extend(['pinv_wexog', 'wendog', 'wexog', 'weights'])``File ~/.local/lib/python3.10/site-packages/statsmodels/base/model.py:267, in LikelihoodModel.init(self, endog, exog, **kwargs)` ` 266 def __init__(self, endog, exog=None, **kwargs):` `--> 267 super().__init__(endog, exog, **kwargs)` ` 268 self.initialize()` File ~/.local/lib/python3.10/site-packages/statsmodels/base/model.py:92, in Model.__init__(self, endog, exog, **kwargs) 90 missing = kwargs.pop('missing', 'none') 91 hasconst = kwargs.pop('hasconst', None) ---> 92 self.data = self._handle_data(endog, exog, missing, hasconst, 93 **kwargs) 94 self.k_constant = self.data.k_constant 95 self.exog = self.data.exog `File ~/.local/lib/python3.10/site-packages/statsmodels/base/model.py:132, in Model._handle_data(self, endog, exog, missing,hasconst, **kwargs) 131 def _handle_data(self, endog, exog, missing, hasconst, **kwargs): --> 132 data = handle_data(endog, exog, missing, hasconst, **kwargs) 133 # kwargs arrays could have changed, easier to just attach here 134 for key in kwargs:``File ~/.local/lib/python3.10/site-packages/statsmodels/base/data.py:700, in handle_data(endog, exog, missing, hasconst, **kwargs)` ` 697 exog = np.asarray(exog)` ` 699 klass = handle_data_class_factory(endog, exog)` `--> 700 return klass(endog, exog=exog, missing=missing, hasconst=hasconst,` ` 701 **kwargs)` File ~/.local/lib/python3.10/site-packages/statsmodels/base/data.py:88, in ModelData.__init__(self, endog, exog, missing, ``hasconst, **kwargs) 86 self.const_idx = None 87 self.k_constant = 0 ---> 88 self._handle_constant(hasconst) 89 self._check_integrity() 90 self._cache = {} `File ~/.local/lib/python3.10/site-packages/statsmodels/base/data.py:134, in ModelData._handle_constant(self, hasconst)` ` 132 exog_max = np.max(self.exog, axis=0)` ` 133 if not np.isfinite(exog_max).all():` `--> 134 raise MissingDataError('exog contains inf or nans')` ` 135 exog_min = np.min(self.exog, axis=0)` ` 136 const_idx = np.where(exog_max == exog_min)[0].squeeze()` MissingDataError: exog contains inf or nans `train_log = np.log(`
---------------------------------------------------------------------------
MissingDataError Traceback (most recent call last)
Input In [102], in <cell line: 27>()
25 else:
26 print("Weak evidence against null hypothesis, time series is non-stationary ")
---> 27 test_stationarity(train['Close'])
`Input In [102], in test_stationarity(timeseries)` ` 16 plt.show(block = False)` ` 18 print('Results of dickey fuller test')` `---> 19 result = adfuller(timeseries, autolag = 'AIC')` ` 20 labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']` ` 21 for value,label in zip(result, labels):`
File ~/.local/lib/python3.10/site-packages/statsmodels/tsa/stattools.py:321, in adfuller(x, maxlag, regression, autolag, store,regresults)
315 # 1 for level
316 # search for lag length with smallest information criteria
317 # Note: use the same number of observations to have comparable IC
318 # aic and bic: smaller is better
320 if not regresults:
--> 321 icbest, bestlag = _autolag(
322 OLS, xdshort, fullRHS, startlag, maxlag, autolag
323 )
324 else:
325 icbest, bestlag, alres = _autolag(
326 OLS,
327 xdshort,
(...)
332 regresults=regresults,
333 )
`File ~/.local/lib/python3.10/site-packages/statsmodels/tsa/stattools.py:129, in _autolag(mod, endog, exog, startlag, maxlag,
``
missing, hasconst, **kwargs)` ` 903 msg = ("Weights are not supported in OLS and will be ignored"` ` 904 "An exception will be raised in the next version.")` ` 905 warnings.warn(msg, ValueWarning)` `--> 906 super(OLS, self).__init__(endog, exog, missing=missing,` ` 907 hasconst=hasconst, **kwargs)` ` 908 if "weights" in self._init_keys:` ` 909 self._init_keys.remove("weights")`
File ~/.local/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:733, in WLS.__init__(self, endog, exog, ``weights, missing, hasconst, **kwargs)
731 else:
732 weights = weights.squeeze()
--> 733 super(WLS, self).__init__(endog, exog, missing=missing,
734 weights=weights, hasconst=hasconst, **kwargs)
735 nobs = self.exog.shape[0]
736 weights = self.weights
`File ~/.local/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:190, in RegressionModel.__init__(self, endog,
**kwargs)` ` 266 def __init__(self, endog, exog=None, **kwargs):` `--> 267 super().__init__(endog, exog, **kwargs)` ` 268 self.initialize()`
File ~/.local/lib/python3.10/site-packages/statsmodels/base/model.py:92, in Model.__init__(self, endog, exog, **kwargs)
90 missing = kwargs.pop('missing', 'none')
91 hasconst = kwargs.pop('hasconst', None)
---> 92 self.data = self._handle_data(endog, exog, missing, hasconst,
93 **kwargs)
94 self.k_constant = self.data.k_constant
95 self.exog = self.data.exog
`File ~/.local/lib/python3.10/site-packages/statsmodels/base/model.py:132, in Model._handle_data(self, endog, exog, missing,
**kwargs)` ` 697 exog = np.asarray(exog)` ` 699 klass = handle_data_class_factory(endog, exog)` `--> 700 return klass(endog, exog=exog, missing=missing, hasconst=hasconst,` ` 701 **kwargs)`
File ~/.local/lib/python3.10/site-packages/statsmodels/base/data.py:88, in ModelData.__init__(self, endog, exog, missing, ``hasconst, **kwargs)
86 self.const_idx = None
87 self.k_constant = 0
---> 88 self._handle_constant(hasconst)
89 self._check_integrity()
90 self._cache = {}
`File ~/.local/lib/python3.10/site-packages/statsmodels/base/data.py:134, in ModelData._handle_constant(self, hasconst)` ` 132 exog_max = np.max(self.exog, axis=0)` ` 133 if not np.isfinite(exog_max).all():` `--> 134 raise MissingDataError('exog contains inf or nans')` ` 135 exog_min = np.min(self.exog, axis=0)` ` 136 const_idx = np.where(exog_max == exog_min)[0].squeeze()`
MissingDataError: exog contains inf or nans
`train_log = np.log(`
I give up for formatting. God knows, how this type of error stack can be provided to developer. Height of frustration.
The text was updated successfully, but these errors were encountered:
No branches or pull requests
I added below :
import statsmodels
from statsmodels.tsa.stattools import adfuller
in first input.
Then I tested the code till :
#Stationarity test
def test_stationarity(timeseries):
...
...
I got stuck with this error message :
Results of dickey fuller test
---------------------------------------------------------------------------
MissingDataError Traceback (most recent call last)
Input In [102], in <cell line: 27>()
25 else:
26 print("Weak evidence against null hypothesis, time series is non-stationary ")
---> 27 test_stationarity(train['Close'])
`Input In [102], in test_stationarity(timeseries)` ` 16 plt.show(block = False)` ` 18 print('Results of dickey fuller test')` `---> 19 result = adfuller(timeseries, autolag = 'AIC')` ` 20 labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']` ` 21 for value,label in zip(result, labels):`
File ~/.local/lib/python3.10/site-packages/statsmodels/tsa/stattools.py:321, in adfuller(x, maxlag, regression, autolag, store,regresults)
315 # 1 for level
316 # search for lag length with smallest information criteria
317 # Note: use the same number of observations to have comparable IC
318 # aic and bic: smaller is better
320 if not regresults:
--> 321 icbest, bestlag = _autolag(
322 OLS, xdshort, fullRHS, startlag, maxlag, autolag
323 )
324 else:
325 icbest, bestlag, alres = _autolag(
326 OLS,
327 xdshort,
(...)
332 regresults=regresults,
333 )
`File ~/.local/lib/python3.10/site-packages/statsmodels/tsa/stattools.py:129, in _autolag(mod, endog, exog, startlag, maxlag,
method, modargs, fitargs, regresults)``
File ~/.local/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:906, in OLS.init(self, endog, exog,missing, hasconst, **kwargs)` ` 903 msg = ("Weights are not supported in OLS and will be ignored"` ` 904 "An exception will be raised in the next version.")` ` 905 warnings.warn(msg, ValueWarning)` `--> 906 super(OLS, self).__init__(endog, exog, missing=missing,` ` 907 hasconst=hasconst, **kwargs)` ` 908 if "weights" in self._init_keys:` ` 909 self._init_keys.remove("weights")`
File ~/.local/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:733, in WLS.__init__(self, endog, exog, ``weights, missing, hasconst, **kwargs)
731 else:
732 weights = weights.squeeze()
--> 733 super(WLS, self).__init__(endog, exog, missing=missing,
734 weights=weights, hasconst=hasconst, **kwargs)
735 nobs = self.exog.shape[0]
736 weights = self.weights
`File ~/.local/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:190, in RegressionModel.__init__(self, endog,
exog, **kwargs)``
File ~/.local/lib/python3.10/site-packages/statsmodels/base/model.py:267, in LikelihoodModel.init(self, endog, exog,**kwargs)` ` 266 def __init__(self, endog, exog=None, **kwargs):` `--> 267 super().__init__(endog, exog, **kwargs)` ` 268 self.initialize()`
File ~/.local/lib/python3.10/site-packages/statsmodels/base/model.py:92, in Model.__init__(self, endog, exog, **kwargs)
90 missing = kwargs.pop('missing', 'none')
91 hasconst = kwargs.pop('hasconst', None)
---> 92 self.data = self._handle_data(endog, exog, missing, hasconst,
93 **kwargs)
94 self.k_constant = self.data.k_constant
95 self.exog = self.data.exog
`File ~/.local/lib/python3.10/site-packages/statsmodels/base/model.py:132, in Model._handle_data(self, endog, exog, missing,
hasconst, **kwargs)``
File ~/.local/lib/python3.10/site-packages/statsmodels/base/data.py:700, in handle_data(endog, exog, missing, hasconst,**kwargs)` ` 697 exog = np.asarray(exog)` ` 699 klass = handle_data_class_factory(endog, exog)` `--> 700 return klass(endog, exog=exog, missing=missing, hasconst=hasconst,` ` 701 **kwargs)`
File ~/.local/lib/python3.10/site-packages/statsmodels/base/data.py:88, in ModelData.__init__(self, endog, exog, missing, ``hasconst, **kwargs)
86 self.const_idx = None
87 self.k_constant = 0
---> 88 self._handle_constant(hasconst)
89 self._check_integrity()
90 self._cache = {}
`File ~/.local/lib/python3.10/site-packages/statsmodels/base/data.py:134, in ModelData._handle_constant(self, hasconst)` ` 132 exog_max = np.max(self.exog, axis=0)` ` 133 if not np.isfinite(exog_max).all():` `--> 134 raise MissingDataError('exog contains inf or nans')` ` 135 exog_min = np.min(self.exog, axis=0)` ` 136 const_idx = np.where(exog_max == exog_min)[0].squeeze()`
MissingDataError: exog contains inf or nans
`train_log = np.log(`
I give up for formatting. God knows, how this type of error stack can be provided to developer. Height of frustration.
The text was updated successfully, but these errors were encountered: