title: auto_arima - Schur decomposition solver error error
body: <p>I'm following <a href="https://towardsdatascience.com/anomaly-detection-with-time-series-forecasting-c34c6d04b24a" rel="nofollow noreferrer">this</a> article to write an anomaly detection program, and my stepwise model seems to be generating <code>LinAlgError: Schur decomposition solver error.</code> I've seen <a href="https://stackoverflow.com/questions/55109912/how-to-solve-linalgerror-valueerror-when-training-arima-model-with-python/55153918">people</a> have the same issue with the ARIMA function, and have tried adding the parameters <code>enforce_stationarity=False, enforce_invertibility=False</code> but the problem persists.</p>
  <pre><code>from pmdarima.arima import auto_arima
  import math
  import statsmodels.api as sm
  import statsmodels.tsa.api as smt
  from sklearn.metrics import mean_squared_error
  stepwise_model = auto_arima(train_log, start_p=1, start_q=1,
                             max_p=3, max_q=3, m=7,
                             start_P=0, seasonal=True,
                             d=1, D=1, trace=True,
                             error_action='ignore',  
                             suppress_warnings=True, 
                             stepwise=True)
  
  train, test = actual_vals[0:-70], actual_vals[-70:]
  train_log, test_log = np.log10(train), np.log10(test)
  # split data into train and test-sets
  history = [x for x in train_log]
  predictions = list()
  predict_log=list()
  for t in range(len(test_log)):
      stepwise_model.fit(history)
      output = stepwise_model.predict(n_periods=1)
      predict_log.append(output[0])
      yhat = 10**output[0]
      predictions.append(yhat)
      obs = test_log[t]
      history.append(obs)
  
  # plot
  figsize=(12, 7)
  plt.figure(figsize=figsize)
  pyplot.plot(test,label='Actuals')
  pyplot.plot(predictions, color='red',label='Predicted')
  pyplot.legend(loc='upper right')
  pyplot.show()
  </code></pre>
  <p>You should be able to replicate the error by replacing actual_vals with the value column of a time series data frame? But if not, my data is <a href="https://drive.google.com/file/d/1Br5_PcjXyyoYjM1KpW8Q3cVYtXPGHMK0/view?usp=sharing" rel="nofollow noreferrer">here</a>. You can read it with <code>df=pd.read_csv('/Users/main/Downloads/Stage.csv', header=14,low_memory=False)</code>. Although I encountered some errors working with the csv data, so you might have to read it from an sql database.</p>