statsmodels ARIMA 模型中的 LU 分解错误

Question

I know there is a very similar question and answer on stackoverflow ( here ), but this seems to be distinctly different. I am using statsmodels v 0.13.2, and I am using an ARIMA model as opposed to a SARIMAX model.

I am trying to fit a list of time series data sets with an ARIMA model. The offending piece of my code is here:

import numpy as np
from statsmodels.tsa.arima.model import ARIMA

items = np.log(og_items)
items['count'] = items['count'].apply(lambda x: 0 if math.isnan(x) or math.isinf(x) else x)
model = ARIMA(items, order=(14, 0, 7))
trained = model.fit()

items is a dataframe containing a date index and a single column, count .

I apply the lambda on the second line because some counts can be 0, resulting in a negative infinity after log is applied. The final product going into the ARIMA does not contain any NaNs or Infinite numbers. However, when I try this without using the log function, I do not get the error. This only occurs on certain series, but there does not seem to be rhyme or reason to which are affected. One series had about half of its values as zero after applying the lambda, while another did not have a single zero. Here is the error:

Traceback (most recent call last):
  File "item_pipeline.py", line 267, in <module>
    main()
  File "item_pipeline.py", line 234, in main
    restaurant_predictions = make_predictions(restaurant_data=restaurant_data, models=models,
  File "item_pipeline.py", line 138, in make_predictions
    predictions = model(*data_tuple[:2], min_date=min_date, max_date=max_date,
  File "/Users/rob/Projects/5out-ml/models/item_level/items/predict_arima.py", line 127, in predict_daily_arima
    predict_date_arima(prediction_dict, item_dict, prediction_date, x_days_out=x_days_out, log_vals=log_vals,
  File "/Users/rob/Projects/5out-ml/models/item_level/items/predict_arima.py", line 51, in predict_date_arima
    raise e
  File "/Users/rob/Projects/5out-ml/models/item_level/items/predict_arima.py", line 47, in predict_date_arima
    fitted = model.fit()
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/arima/model.py", line 390, in fit
    res = super().fit(
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 704, in fit
    mlefit = super(MLEModel, self).fit(start_params, method=method,
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/base/model.py", line 563, in fit
    xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/base/optimizer.py", line 241, in _fit
    xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/base/optimizer.py", line 651, in _fit_lbfgs
    retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py", line 199, in fmin_l_bfgs_b
    res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_lbfgsb_py.py", line 362, in _minimize_lbfgsb
    f, g = func_and_grad(x)
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py", line 286, in fun_and_grad
    self._update_grad()
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py", line 256, in _update_grad
    self._update_grad_impl()
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py", line 173, in update_grad
    self.g = approx_derivative(fun_wrapped, self.x, f0=self.f,
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_numdiff.py", line 505, in approx_derivative
    return _dense_difference(fun_wrapped, x0, f0, h,
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_numdiff.py", line 576, in _dense_difference
    df = fun(x) - f0
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_numdiff.py", line 456, in fun_wrapped
    f = np.atleast_1d(fun(x, *args, **kwargs))
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/scipy/optimize/_differentiable_functions.py", line 137, in fun_wrapped
    fx = fun(np.copy(x), *args)
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/base/model.py", line 531, in f
    return -self.loglike(params, *args) / nobs
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 939, in loglike
    loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py", line 983, in loglike
    kfilter = self._filter(**kwargs)
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/kalman_filter.py", line 903, in _filter
    self._initialize_state(prefix=prefix, complex_step=complex_step)
  File "/Users/rob/Projects/5out-ml/venv/lib/python3.8/site-packages/statsmodels/tsa/statespace/representation.py", line 983, in _initialize_state
    self._statespaces[prefix].initialize(self.initialization,
  File "statsmodels/tsa/statespace/_representation.pyx", line 1362, in statsmodels.tsa.statespace._representation.dStatespace.initialize
  File "statsmodels/tsa/statespace/_initialization.pyx", line 288, in statsmodels.tsa.statespace._initialization.dInitialization.initialize
  File "statsmodels/tsa/statespace/_initialization.pyx", line 406, in statsmodels.tsa.statespace._initialization.dInitialization.initialize_stationary_stationary_cov
  File "statsmodels/tsa/statespace/_tools.pyx", line 1206, in statsmodels.tsa.statespace._tools._dsolve_discrete_lyapunov
numpy.linalg.LinAlgError: LU decomposition error.

The solution in the other stackoverflow post was to initialize the statespace differently. It looks like the statespace is involved, if you look at the last few lines of the error. However, it does not seem that that workflow is exposed in the newer version of statsmodels. Is it? If not, what else can I try to circumvent this error?

So far, I have tried manually initializing the model to approximate diffuse , and manually setting the initialize property to approximate diffuse . Neither seem to be valid in the new statsmodels code.

Answer 1

Turns out there's a new way to initialize. The second line below is the operative line.

model = ARIMA(items, order=(14, 0, 7))
model.initialize_approximate_diffuse() # this line
trained = model.fit()