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python - VARMAX 结果扩展导致 ValueError: array must not contain infs or NaNs

问题描述

我正在尝试使用新的观察结果来扩展VARMAX模型，以便进行前向验证。

但是，我VARMAXResults.extend()正在抛出一个ValueError.

可重现的例子：

#---- 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很好：

#-------- 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

标签： pythontime-seriesstatsmodelsvarmax

解决方案

单变量SARIMAX的默认趋势是'n'（对于“无”）。矢量情况VARMAX中的默认趋势是'c'（对于“常量”）。因此，根据您的需要，执行以下任一操作：

  1. trend='n'如果您不想要趋势组件，只需设置。（这实际上是我想要的，但没有意识到它不是 VARMAX 的默认参数。）或者......

  2. 设置trend = 'n' 并使用该exog=参数作为VARMAX 中趋势组件错误的解决方法。感谢statsmodels贡献者 ChadFulton，他推荐了这种方法直到修复。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])       # <----

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