title: No suitable ARIMA models found
body: <p>I have the following <a href="https://pastebin.com/gG157Egz" rel="nofollow noreferrer">data</a> and trying to run <a href="https://pastebin.com/Vd2bTw29" rel="nofollow noreferrer">arima_standard and arima_fourier</a>. I am backtesting so I am running the forecast for a couple of dates. In this case I am running it as of September 2018, hence the training data is between <code>2016-01-29</code> and <code>2017-09-30</code> and the testing data is between <code>2017-10-01</code> and <code>2018-09-30</code>. </p>
  
  <pre><code>    how_many_weeks_test &lt;- 52
      how_many_days_test &lt;- 365
      temp_fcst_train_data &lt;- head(temp_fcast_data, -1 * how_many_days_test)
      temp_fcst_test_data &lt;-  tail(temp_fcast_data, how_many_days_test)
  </code></pre>
  
  <p>Column <code>j</code> is a variable <code>var</code> in data file, there are multiple variables I am forecasting but only this one has these issuse hence I provided the data only for this one. </p>
  
  <pre><code>temp_xreg_cols &lt;- c(names(temp_fcst_train_data[, grepl("special_events",names(temp_fcst_train_data))]),  paste("day_fluct_", j, sep = ''))
    temp_xreg_cols2 &lt;- names(temp_fcst_train_data[, grepl("month",names(temp_fcst_train_data)) | grepl("wday",names(temp_fcst_train_data)) | grepl("special_events",names(temp_fcst_train_data))])
    temp_model1 &lt;- arima_fourier_train(train = temp_fcst_train_data, test = temp_fcst_test_data, column = j, freq = 364)
    temp_model2 &lt;- arima_standard_train(train = temp_fcst_train_data, test = temp_fcst_test_data, column = j , freq = 364, xreg_cols = temp_xreg_cols)
    temp_model3 &lt;- arima_standard_train(train = temp_fcst_train_data, test = temp_fcst_test_data, column = j , freq = 364, xreg_cols = temp_xreg_cols2)
  </code></pre>
  
  <p>When running <code>arima_fourier_train</code> or <code>arima_standard_train</code>, this is the error I am getting. </p>
  
  <pre><code>Residual standard error: 948600000 on 586 degrees of freedom
  Multiple R-squared:  0.4815,    Adjusted R-squared:  0.4602 
  F-statistic: 22.67 on 24 and 586 DF,  p-value: &lt; 2.2e-16
  
  
   Fitting models using approximations to speed things up...
  
   ARIMA(2,1,2) with drift         : Inf
   ARIMA(0,1,0) with drift         : Inf
   ARIMA(1,1,0) with drift         : Inf
   ARIMA(0,1,1) with drift         : Inf
   ARIMA(0,1,0)                    : Inf
   ARIMA(1,1,2) with drift         : Inf
   ARIMA(2,1,1) with drift         : Inf
   ARIMA(3,1,2) with drift         : Inf
   ARIMA(2,1,3) with drift         : Inf
   ARIMA(1,1,1) with drift         : Inf
   ARIMA(1,1,3) with drift         : Inf
   ARIMA(3,1,1) with drift         : Inf
   ARIMA(3,1,3) with drift         : Inf
   ARIMA(2,1,2)                    : Inf
  
  Error in auto.arima(y, xreg = xreg, seasonal = FALSE, max.d = 5, num.cores = 6,  : 
    No suitable ARIMA model found
  [1] "this model will be ignored"
  </code></pre>
  
  <p>Is there a way how to improve hyper-parameters to get the forecast or the issue is something else? I am new to forcecasting and find it difficult to understand why the error.</p>
  
  <p>If I am running SARIMA (i.e. seasonal parameter is set to TRUE) and this is the error I am getting:</p>
  
  <pre><code>Error in auto.arima(y, xreg = xreg, seasonal = TRUE, max.d = 5, num.cores = 6,  : 
    No suitable ARIMA model found
  In addition: Warning message:
  The chosen seasonal unit root test encountered an error when testing for the first difference.
  From stl(): series is not periodic or has less than two periods
  0 seasonal differences will be used. Consider using a different unit root test. 
  [1] "this model will be ignored"
  </code></pre>
  
  <p>What are your thoughts? I am using also NNs and TBATS but the error is between 30% and 40%, I would like to have an error below 20%, or ideally below 15%.</p>
  
  <p>Any help or advice is welcome!</p>
  
  <h2>Thanks!</h2>