fitting ARIMA model including exogeneos predictor getting DLASCL error - python-3.x

Question

I have two pandas dataframes. the dataframes are named stationary_train and test_exog. I have the shape of the dataframes below. I also have some sample data from the dataframes. I'm trying to fit an arima model to the data using ARIMA from statsmodel. I want to forecast the endogeneous variable "stationary_train" using exogeneous variable "test_exog". I'm using the code below. I'm getting the error below, I'm unclear why. the two dataframes are the same shape, and I don't see any missing values in either dataframe. can anyone see what the issue is and suggest how to fix it?
data:
test_exog[:-1].shape
(203, 1)
stationary_train.shape
(203, 1)
print(exog_auto_model.order)
(11, 0, 6)
print(test_exog[:-1].head())
exog_passengers
month
2000-01-01 46513.9
2000-02-01 48555.7
2000-03-01 58812.4
2000-04-01 56101.1
2000-05-01 58237.4
print(stationary_train.head())
passengers
month
2000-02-01 2034.0
2000-03-01 10238.0
2000-04-01 -2731.0
2000-05-01 2168.0
2000-06-01 2872.0
code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# statmodels
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.arima_model import ARMA, ARIMA
# datetime
from datetime import datetime
ARIMA(endog=stationary_train.values.reshape(-1,1),
exog=test_exog[:-1],
order=exog_auto_model.order).fit()
error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-90-ae6d07b32cf7> in <module>
1 exog_predict= ARIMA(endog=stationary_train.values.reshape(-1,1),
2 exog=test_exog[:-1],
----> 3 order=exog_auto_model.order).fit()
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/tsa/arima_model.py in fit(self, start_params, trend, method, transparams, solver, maxiter, full_output, disp, callback, start_ar_lags, **kwargs)
1028 maxiter=maxiter,
1029 full_output=full_output, disp=disp,
-> 1030 callback=callback, **kwargs)
1031 params = mlefit.params
1032
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/base/model.py in fit(self, start_params, method, maxiter, full_output, disp, fargs, callback, retall, skip_hessian, **kwargs)
525 callback=callback,
526 retall=retall,
--> 527 full_output=full_output)
528
529 # NOTE: this is for fit_regularized and should be generalized
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/base/optimizer.py in _fit(self, objective, gradient, start_params, fargs, kwargs, hessian, method, maxiter, full_output, disp, callback, retall)
225 disp=disp, maxiter=maxiter, callback=callback,
226 retall=retall, full_output=full_output,
--> 227 hess=hessian)
228
229 optim_settings = {'optimizer': method, 'start_params': start_params,
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/base/optimizer.py in _fit_lbfgs(f, score, start_params, fargs, kwargs, disp, maxiter, callback, retall, full_output, hess)
630 callback=callback, args=fargs,
631 bounds=bounds, disp=disp,
--> 632 **extra_kwargs)
633
634 if full_output:
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py in fmin_l_bfgs_b(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)
196
197 res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
--> 198 **opts)
199 d = {'grad': res['jac'],
200 'task': res['message'],
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/lbfgsb.py in _minimize_lbfgsb(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, finite_diff_rel_step, **unknown_options)
306 sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps,
307 bounds=new_bounds,
--> 308 finite_diff_rel_step=finite_diff_rel_step)
309
310 func_and_grad = sf.fun_and_grad
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/optimize.py in _prepare_scalar_function(fun, x0, jac, args, bounds, epsilon, finite_diff_rel_step, hess)
260 # calculation reduces overall function evaluations.
261 sf = ScalarFunction(fun, x0, args, grad, hess,
--> 262 finite_diff_rel_step, bounds, epsilon=epsilon)
263
264 return sf
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in __init__(self, fun, x0, args, grad, hess, finite_diff_rel_step, finite_diff_bounds, epsilon)
74
75 self._update_fun_impl = update_fun
---> 76 self._update_fun()
77
78 # Gradient evaluation
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in _update_fun(self)
164 def _update_fun(self):
165 if not self.f_updated:
--> 166 self._update_fun_impl()
167 self.f_updated = True
168
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in update_fun()
71
72 def update_fun():
---> 73 self.f = fun_wrapped(self.x)
74
75 self._update_fun_impl = update_fun
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/scipy/optimize/_differentiable_functions.py in fun_wrapped(x)
68 def fun_wrapped(x):
69 self.nfev += 1
---> 70 return fun(x, *args)
71
72 def update_fun():
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/base/model.py in f(params, *args)
499
500 def f(params, *args):
--> 501 return -self.loglike(params, *args) / nobs
502
503 if method == 'newton':
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/tsa/arima_model.py in loglike(self, params, set_sigma2)
839 method = self.method
840 if method in ['mle', 'css-mle']:
--> 841 return self.loglike_kalman(params, set_sigma2)
842 elif method == 'css':
843 return self.loglike_css(params, set_sigma2)
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/tsa/arima_model.py in loglike_kalman(self, params, set_sigma2)
849 Compute exact loglikelihood for ARMA(p,q) model by the Kalman Filter.
850 """
--> 851 return KalmanFilter.loglike(params, self, set_sigma2)
852
853 def loglike_css(self, params, set_sigma2=True):
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/statsmodels/tsa/kalmanf/kalmanfilter.py in loglike(cls, params, arma_model, set_sigma2)
218 loglike, sigma2 = kalman_loglike.kalman_loglike_double(
219 y, k, k_ar, k_ma, k_lags, int(nobs),
--> 220 Z_mat, R_mat, T_mat)
221 elif np.issubdtype(paramsdtype, np.complex128):
222 loglike, sigma2 = kalman_loglike.kalman_loglike_complex(
statsmodels/tsa/kalmanf/kalman_loglike.pyx in statsmodels.tsa.kalmanf.kalman_loglike.kalman_loglike_double()
statsmodels/tsa/kalmanf/kalman_loglike.pyx in statsmodels.tsa.kalmanf.kalman_loglike.kalman_filter_double()
<__array_function__ internals> in pinv(*args, **kwargs)
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/numpy/linalg/linalg.py in pinv(a, rcond, hermitian)
2001 return wrap(res)
2002 a = a.conjugate()
-> 2003 u, s, vt = svd(a, full_matrices=False, hermitian=hermitian)
2004
2005 # discard small singular values
<__array_function__ internals> in svd(*args, **kwargs)
~/anaconda3/envs/arima_forecast/lib/python3.6/site-packages/numpy/linalg/linalg.py in svd(a, full_matrices, compute_uv, hermitian)
1659
1660 signature = 'D->DdD' if isComplexType(t) else 'd->ddd'
-> 1661 u, s, vh = gufunc(a, signature=signature, extobj=extobj)
1662 u = u.astype(result_t, copy=False)
1663 s = s.astype(_realType(result_t), copy=False)
ValueError: On entry to DLASCL parameter number 4 had an illegal value