我正试图用新的观测结果扩展VARMAX模型，以便进行正向行走验证。
但是，我的VARMAXResults.extend()正在抛出一个ValueError。
可复制示例：
#---- This returns: ValueError: array must not contain infs or NaNs ----------
from statsmodels.tsa.statespace.varmax import VARMAX
import numpy as np
np.random.seed(1)
y_hist = 100*np.random.rand(50,2)
model = VARMAX(endog= y_hist,order=(2,0)).fit()
print("VARMAX model summary")
print(model.summary())
next_y_hat = model.forecast()
print("nPredicted next value")
print(next_y_hat)
# simulate next observed value
next_y = next_y_hat
# extend model
model = model.extend(endog = next_y) # ValueError
请注意，这种方法在使用SARIMAX:的单变量情况下效果良好
#-------- This works fine: -------------
from statsmodels.tsa.statespace.sarimax import SARIMAX
uni_model = SARIMAX(endog=y_hist[:,1],order=(2,0,0)).fit()
print("SARIMAX model summary")
print(uni_model.summary())
next_y_hat_uni = uni_model.forecast()
print("nPredicted next value")
print(next_y_hat_uni)
# simulate next observed value
next_y_uni = next_y_hat_uni
# extend model
uni_model = uni_model.extend(endog = next_y_uni) # no ValueError
版本：statsmodels v0.11.1，numpy 1.16.3。
追溯：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-93-347df5f5ac07> in <module>
16
17 # try to update model
---> 18 model = model.extend(endog = next_y)
19 # returns ValueError: array must not contain infs or NaNs
/usr/local/lib/python3.7/site-packages/statsmodels/tsa/statespace/varmax.py in extend(self, endog, exog, **kwargs)
915
916 if self.smoother_results is not None:
--> 917 res = mod.smooth(self.params)
918 else:
919 res = mod.filter(self.params)
/usr/local/lib/python3.7/site-packages/statsmodels/tsa/statespace/mlemodel.py in smooth(self, params, transformed, includes_fixed, complex_step, cov_type, cov_kwds, return_ssm, results_class, results_wrapper_class, **kwargs)
839 return self._wrap_results(params, result, return_ssm, cov_type,
840 cov_kwds, results_class,
--> 841 results_wrapper_class)
842
843 _loglike_param_names = ['transformed', 'includes_fixed', 'complex_step']
/usr/local/lib/python3.7/site-packages/statsmodels/tsa/statespace/mlemodel.py in _wrap_results(self, params, result, return_raw, cov_type, cov_kwds, results_class, wrapper_class)
736 wrapper_class = self._res_classes['fit'][1]
737
--> 738 res = results_class(self, params, result, **result_kwargs)
739 result = wrapper_class(res)
740 return result
/usr/local/lib/python3.7/site-packages/statsmodels/tsa/statespace/varmax.py in __init__(self, model, params, filter_results, cov_type, cov_kwds, **kwargs)
854 cov_kwds=None, **kwargs):
855 super(VARMAXResults, self).__init__(model, params, filter_results,
--> 856 cov_type, cov_kwds, **kwargs)
857
858 self.specification = Bunch(**{
/usr/local/lib/python3.7/site-packages/statsmodels/tsa/statespace/mlemodel.py in __init__(self, model, params, results, cov_type, cov_kwds, **kwargs)
2274 'smoothed_state_disturbance_cov']
2275 for name in extra_arrays:
-> 2276 setattr(self, name, getattr(self.filter_results, name, None))
2277
2278 # Remove too-short results when memory conservation was used
/usr/local/lib/python3.7/site-packages/statsmodels/tsa/statespace/kalman_filter.py in standardized_forecasts_error(self)
1914 linalg.solve_triangular(
1915 upper, self.forecasts_error[mask, t],
-> 1916 trans=1))
1917 except linalg.LinAlgError:
1918 self._standardized_forecasts_error[mask, t] = (
/usr/local/lib/python3.7/site-packages/scipy/linalg/basic.py in solve_triangular(a, b, trans, lower, unit_diagonal, overwrite_b, debug, check_finite)
334
335 a1 = _asarray_validated(a, check_finite=check_finite)
--> 336 b1 = _asarray_validated(b, check_finite=check_finite)
337 if len(a1.shape) != 2 or a1.shape[0] != a1.shape[1]:
338 raise ValueError('expected square matrix')
/usr/local/lib/python3.7/site-packages/scipy/_lib/_util.py in _asarray_validated(a, check_finite, sparse_ok, objects_ok, mask_ok, as_inexact)
237 raise ValueError('masked arrays are not supported')
238 toarray = np.asarray_chkfinite if check_finite else np.asarray
--> 239 a = toarray(a)
240 if not objects_ok:
241 if a.dtype is np.dtype('O'):
/usr/local/lib/python3.7/site-packages/numpy/lib/function_base.py in asarray_chkfinite(a, dtype, order)
496
497 @array_function_dispatch(_piecewise_dispatcher)
--> 498 def piecewise(x, condlist, funclist, *args, **kw):
499 """
500 Evaluate a piecewise-defined function.
ValueError: array must not contain infs or NaNs
单变量SARIMAX中的默认趋势为'n'(表示"无"(。向量情况VARMAX中的默认趋势是'c'(对于"常数"(。所以，根据你想要什么，做任何一个：
-
如果不需要趋势组件，只需设置
trend='n'即可。(这实际上是我想要的，但没有意识到这不是VARMAX的默认参数。(或者。。。 -
设置
trend = 'n'和使用exog=参数作为VARMAX中趋势组件错误的解决方法。感谢statsmodels的贡献者ChadFulton，他推荐了这种方法，直到修复为止。当你想要一个恒定的趋势时，他的解决方案：
import numpy as np
np.random.seed(1)
y_hist = 100*np.random.rand(50,2)
model = VARMAX(endog= y_hist,order=(2,0),
trend='n', # <---- notice
exog=np.ones(y_hist.shape[0])).fit() # <----
print("VARMAX model summary")
print(model.summary())
next_y_hat = model.forecast(exog=[1])
print("nPredicted next value")
print(next_y_hat)
# simulate next observed value
next_y = next_y_hat
# extend model
model = model.extend(endog = next_y, exog=[1]) # <----