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Can i forecast with discontinued data using SARIMA



The Next CEO of Stack Overflow
2019 Community Moderator ElectionTime series forecast using SVM?How do I use rnn to forecast to n periods with limited data?Forecast vs Prediction: What is the difference?How to use features when predicting aggregation?Analysing spikes in demand to forecast future demanddemand forecast for B2BWhat Machine Learning Algorithm could I use to determine some measure in a date?forecast product demand in one week using machine learning approachContacts/Issues forecast based on orders










0












$begingroup$


I have data for sales on monthly basis but few months information is not in csv file, Can i forecast or fill that missing month with other calculated value from present record.



enter image description here



part of code i am using:



AIC = []
SARIMAX_model = []
for param in pdq:
 for param_seasonal in seasonal_pdq:
 try:
 mod = sm.tsa.statespace.SARIMAX(train_data,
 order=param,
 seasonal_order=param_seasonal,
 enforce_stationarity=False,
 enforce_invertibility=False)

 results = mod.fit()

 print('SARIMAXx - AIC:'.format(param, param_seasonal, results.aic), end='r')
 AIC.append(results.aic)
 SARIMAX_model.append([param, param_seasonal])
 except:
 continue
print('The smallest AIC is for model SARIMAXx'.format(min(AIC), SARIMAX_model[AIC.index(min(AIC))][0],SARIMAX_model[AIC.index(min(AIC))][1]))

# Let's fit this model
mod = sm.tsa.statespace.SARIMAX(train_data,
 order=SARIMAX_model[AIC.index(min(AIC))][0],
 seasonal_order=SARIMAX_model[AIC.index(min(AIC))][1],
 enforce_stationarity=False,
 enforce_invertibility=False)





python machine-learning-model forecasting







share|improve this question









$endgroup$
















    0












    $begingroup$


    I have data for sales on monthly basis but few months information is not in csv file, Can i forecast or fill that missing month with other calculated value from present record.



    enter image description here



    part of code i am using:



    AIC = []
    SARIMAX_model = []
    for param in pdq:
     for param_seasonal in seasonal_pdq:
     try:
     mod = sm.tsa.statespace.SARIMAX(train_data,
     order=param,
     seasonal_order=param_seasonal,
     enforce_stationarity=False,
     enforce_invertibility=False)

     results = mod.fit()

     print('SARIMAXx - AIC:'.format(param, param_seasonal, results.aic), end='r')
     AIC.append(results.aic)
     SARIMAX_model.append([param, param_seasonal])
     except:
     continue
    print('The smallest AIC is for model SARIMAXx'.format(min(AIC), SARIMAX_model[AIC.index(min(AIC))][0],SARIMAX_model[AIC.index(min(AIC))][1]))

    # Let's fit this model
    mod = sm.tsa.statespace.SARIMAX(train_data,
     order=SARIMAX_model[AIC.index(min(AIC))][0],
     seasonal_order=SARIMAX_model[AIC.index(min(AIC))][1],
     enforce_stationarity=False,
     enforce_invertibility=False)





    python machine-learning-model forecasting







    share|improve this question









    $endgroup$














      0












      0








      0





      $begingroup$


      I have data for sales on monthly basis but few months information is not in csv file, Can i forecast or fill that missing month with other calculated value from present record.



      enter image description here



      part of code i am using:



      AIC = []
      SARIMAX_model = []
      for param in pdq:
       for param_seasonal in seasonal_pdq:
       try:
       mod = sm.tsa.statespace.SARIMAX(train_data,
       order=param,
       seasonal_order=param_seasonal,
       enforce_stationarity=False,
       enforce_invertibility=False)

       results = mod.fit()

       print('SARIMAXx - AIC:'.format(param, param_seasonal, results.aic), end='r')
       AIC.append(results.aic)
       SARIMAX_model.append([param, param_seasonal])
       except:
       continue
      print('The smallest AIC is for model SARIMAXx'.format(min(AIC), SARIMAX_model[AIC.index(min(AIC))][0],SARIMAX_model[AIC.index(min(AIC))][1]))

      # Let's fit this model
      mod = sm.tsa.statespace.SARIMAX(train_data,
       order=SARIMAX_model[AIC.index(min(AIC))][0],
       seasonal_order=SARIMAX_model[AIC.index(min(AIC))][1],
       enforce_stationarity=False,
       enforce_invertibility=False)





      python machine-learning-model forecasting







      share|improve this question









      $endgroup$




      I have data for sales on monthly basis but few months information is not in csv file, Can i forecast or fill that missing month with other calculated value from present record.



      enter image description here



      part of code i am using:



      AIC = []
      SARIMAX_model = []
      for param in pdq:
       for param_seasonal in seasonal_pdq:
       try:
       mod = sm.tsa.statespace.SARIMAX(train_data,
       order=param,
       seasonal_order=param_seasonal,
       enforce_stationarity=False,
       enforce_invertibility=False)

       results = mod.fit()

       print('SARIMAXx - AIC:'.format(param, param_seasonal, results.aic), end='r')
       AIC.append(results.aic)
       SARIMAX_model.append([param, param_seasonal])
       except:
       continue
      print('The smallest AIC is for model SARIMAXx'.format(min(AIC), SARIMAX_model[AIC.index(min(AIC))][0],SARIMAX_model[AIC.index(min(AIC))][1]))

      # Let's fit this model
      mod = sm.tsa.statespace.SARIMAX(train_data,
       order=SARIMAX_model[AIC.index(min(AIC))][0],
       seasonal_order=SARIMAX_model[AIC.index(min(AIC))][1],
       enforce_stationarity=False,
       enforce_invertibility=False)





      python machine-learning-model forecasting




      python machine-learning-model forecasting






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Mar 22 at 5:05









      bipul kumarbipul kumar

      316




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