When should you balance a time series dataset?












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I'm training a machine learning algorithm to classify up/down trends in a time series and I'm using an imbalanced feature set. It seems necessary to balance the data since the algorithm could learn a bias towards a particular trend, but this comes at the cost of a non-representative dataset. Should I balance my data? And if so, is random undersampling the right method?






machine-learning classification time-series unbalanced-classes class-imbalance







share|improve this question











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  • 1




    $begingroup$
    Which types of models do you use? some models are less sensitive to imbalanced datasets
    $endgroup$
    – Omri374
    Feb 24 '18 at 20:41






  • 1




    $begingroup$
    @Omri374: I'm testing an LSTM network, SVM, and Random Forest classifier.
    $endgroup$
    – Jonathan Shobrook
    Feb 25 '18 at 5:28






  • 1




    $begingroup$
    For SVMs and Random Forests, are you using a sliding window to create samples? If yes, you can then perform sampling on the created windows
    $endgroup$
    – Omri374
    Feb 25 '18 at 9:07






  • 1




    $begingroup$
    For LSTMs, you could tweak the loss function. see here: stats.stackexchange.com/questions/197273/… and stackoverflow.com/questions/35155655/…
    $endgroup$
    – Omri374
    Feb 25 '18 at 9:13












  • $begingroup$
    You might want to read this paper
    $endgroup$
    – iso_9001_
    2 days ago
















3












$begingroup$


I'm training a machine learning algorithm to classify up/down trends in a time series and I'm using an imbalanced feature set. It seems necessary to balance the data since the algorithm could learn a bias towards a particular trend, but this comes at the cost of a non-representative dataset. Should I balance my data? And if so, is random undersampling the right method?






machine-learning classification time-series unbalanced-classes class-imbalance







share|improve this question











$endgroup$








  • 1




    $begingroup$
    Which types of models do you use? some models are less sensitive to imbalanced datasets
    $endgroup$
    – Omri374
    Feb 24 '18 at 20:41






  • 1




    $begingroup$
    @Omri374: I'm testing an LSTM network, SVM, and Random Forest classifier.
    $endgroup$
    – Jonathan Shobrook
    Feb 25 '18 at 5:28






  • 1




    $begingroup$
    For SVMs and Random Forests, are you using a sliding window to create samples? If yes, you can then perform sampling on the created windows
    $endgroup$
    – Omri374
    Feb 25 '18 at 9:07






  • 1




    $begingroup$
    For LSTMs, you could tweak the loss function. see here: stats.stackexchange.com/questions/197273/… and stackoverflow.com/questions/35155655/…
    $endgroup$
    – Omri374
    Feb 25 '18 at 9:13












  • $begingroup$
    You might want to read this paper
    $endgroup$
    – iso_9001_
    2 days ago














3












3








3





$begingroup$


I'm training a machine learning algorithm to classify up/down trends in a time series and I'm using an imbalanced feature set. It seems necessary to balance the data since the algorithm could learn a bias towards a particular trend, but this comes at the cost of a non-representative dataset. Should I balance my data? And if so, is random undersampling the right method?






machine-learning classification time-series unbalanced-classes class-imbalance







share|improve this question











$endgroup$




I'm training a machine learning algorithm to classify up/down trends in a time series and I'm using an imbalanced feature set. It seems necessary to balance the data since the algorithm could learn a bias towards a particular trend, but this comes at the cost of a non-representative dataset. Should I balance my data? And if so, is random undersampling the right method?






machine-learning classification time-series unbalanced-classes class-imbalance




machine-learning classification time-series unbalanced-classes class-imbalance






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Feb 22 '18 at 18:58







Jonathan Shobrook

















asked Feb 22 '18 at 18:10









Jonathan ShobrookJonathan Shobrook

73117




73117








  • 1




    $begingroup$
    Which types of models do you use? some models are less sensitive to imbalanced datasets
    $endgroup$
    – Omri374
    Feb 24 '18 at 20:41






  • 1




    $begingroup$
    @Omri374: I'm testing an LSTM network, SVM, and Random Forest classifier.
    $endgroup$
    – Jonathan Shobrook
    Feb 25 '18 at 5:28






  • 1




    $begingroup$
    For SVMs and Random Forests, are you using a sliding window to create samples? If yes, you can then perform sampling on the created windows
    $endgroup$
    – Omri374
    Feb 25 '18 at 9:07






  • 1




    $begingroup$
    For LSTMs, you could tweak the loss function. see here: stats.stackexchange.com/questions/197273/… and stackoverflow.com/questions/35155655/…
    $endgroup$
    – Omri374
    Feb 25 '18 at 9:13












  • $begingroup$
    You might want to read this paper
    $endgroup$
    – iso_9001_
    2 days ago














  • 1




    $begingroup$
    Which types of models do you use? some models are less sensitive to imbalanced datasets
    $endgroup$
    – Omri374
    Feb 24 '18 at 20:41






  • 1




    $begingroup$
    @Omri374: I'm testing an LSTM network, SVM, and Random Forest classifier.
    $endgroup$
    – Jonathan Shobrook
    Feb 25 '18 at 5:28






  • 1




    $begingroup$
    For SVMs and Random Forests, are you using a sliding window to create samples? If yes, you can then perform sampling on the created windows
    $endgroup$
    – Omri374
    Feb 25 '18 at 9:07






  • 1




    $begingroup$
    For LSTMs, you could tweak the loss function. see here: stats.stackexchange.com/questions/197273/… and stackoverflow.com/questions/35155655/…
    $endgroup$
    – Omri374
    Feb 25 '18 at 9:13












  • $begingroup$
    You might want to read this paper
    $endgroup$
    – iso_9001_
    2 days ago








1




1




$begingroup$
Which types of models do you use? some models are less sensitive to imbalanced datasets
$endgroup$
– Omri374
Feb 24 '18 at 20:41




$begingroup$
Which types of models do you use? some models are less sensitive to imbalanced datasets
$endgroup$
– Omri374
Feb 24 '18 at 20:41




1




1




$begingroup$
@Omri374: I'm testing an LSTM network, SVM, and Random Forest classifier.
$endgroup$
– Jonathan Shobrook
Feb 25 '18 at 5:28




$begingroup$
@Omri374: I'm testing an LSTM network, SVM, and Random Forest classifier.
$endgroup$
– Jonathan Shobrook
Feb 25 '18 at 5:28




1




1




$begingroup$
For SVMs and Random Forests, are you using a sliding window to create samples? If yes, you can then perform sampling on the created windows
$endgroup$
– Omri374
Feb 25 '18 at 9:07




$begingroup$
For SVMs and Random Forests, are you using a sliding window to create samples? If yes, you can then perform sampling on the created windows
$endgroup$
– Omri374
Feb 25 '18 at 9:07




1




1




$begingroup$
For LSTMs, you could tweak the loss function. see here: stats.stackexchange.com/questions/197273/… and stackoverflow.com/questions/35155655/…
$endgroup$
– Omri374
Feb 25 '18 at 9:13






$begingroup$
For LSTMs, you could tweak the loss function. see here: stats.stackexchange.com/questions/197273/… and stackoverflow.com/questions/35155655/…
$endgroup$
– Omri374
Feb 25 '18 at 9:13














$begingroup$
You might want to read this paper
$endgroup$
– iso_9001_
2 days ago




$begingroup$
You might want to read this paper
$endgroup$
– iso_9001_
2 days ago










1 Answer
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$begingroup$

If you can change the Loss function of the algorithm, It will be very helpful and as a result you don't need to down sample your data. There are many useful metrics which were introduced for evaluating the performance of classification methods for imbalanced data-sets. Some of them are Kappa, CEN, MCEN, MCC, and DP.



Disclaimer:



If you use python, PyCM module can help you to find out these metrics.



Here is a simple code to get the recommended parameters from this module:



>>> from pycm import *



>>> cm = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":2}, "Class2": {"Class1": 0, "Class2": 5}})  



>>> print(cm.recommended_list)

["Kappa", "SOA1(Landis & Koch)", "SOA2(Fleiss)", "SOA3(Altman)", "SOA4(Cicchetti)", "CEN", "MCEN", "MCC", "J", "Overall J", "Overall MCC", "Overall CEN", "Overall MCEN", "AUC", "AUCI", "G", "DP", "DPI", "GI"]


After that, each of these parameters you want to use as the loss function can be used as follows:



>>> y_pred = model.predict      #the prediction of the implemented model



>>> y_actu = data.target        #data labels



>>> cm = ConfusionMatrix(y_actu, y_pred)



>>> loss = cm.Kappa             #or any other parameter (Example: cm.SOA1)





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    $begingroup$

    If you can change the Loss function of the algorithm, It will be very helpful and as a result you don't need to down sample your data. There are many useful metrics which were introduced for evaluating the performance of classification methods for imbalanced data-sets. Some of them are Kappa, CEN, MCEN, MCC, and DP.



    Disclaimer:



    If you use python, PyCM module can help you to find out these metrics.



    Here is a simple code to get the recommended parameters from this module:



    >>> from pycm import *
    

    
>>> cm = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":2}, "Class2": {"Class1": 0, "Class2": 5}})  
    

    
>>> print(cm.recommended_list)
    
["Kappa", "SOA1(Landis & Koch)", "SOA2(Fleiss)", "SOA3(Altman)", "SOA4(Cicchetti)", "CEN", "MCEN", "MCC", "J", "Overall J", "Overall MCC", "Overall CEN", "Overall MCEN", "AUC", "AUCI", "G", "DP", "DPI", "GI"]


    After that, each of these parameters you want to use as the loss function can be used as follows:



    >>> y_pred = model.predict      #the prediction of the implemented model
    

    
>>> y_actu = data.target        #data labels
    

    
>>> cm = ConfusionMatrix(y_actu, y_pred)
    

    
>>> loss = cm.Kappa             #or any other parameter (Example: cm.SOA1)





    share|improve this answer











    $endgroup$


















      1












      $begingroup$

      If you can change the Loss function of the algorithm, It will be very helpful and as a result you don't need to down sample your data. There are many useful metrics which were introduced for evaluating the performance of classification methods for imbalanced data-sets. Some of them are Kappa, CEN, MCEN, MCC, and DP.



      Disclaimer:



      If you use python, PyCM module can help you to find out these metrics.



      Here is a simple code to get the recommended parameters from this module:



      >>> from pycm import *
      

      
>>> cm = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":2}, "Class2": {"Class1": 0, "Class2": 5}})  
      

      
>>> print(cm.recommended_list)
      
["Kappa", "SOA1(Landis & Koch)", "SOA2(Fleiss)", "SOA3(Altman)", "SOA4(Cicchetti)", "CEN", "MCEN", "MCC", "J", "Overall J", "Overall MCC", "Overall CEN", "Overall MCEN", "AUC", "AUCI", "G", "DP", "DPI", "GI"]


      After that, each of these parameters you want to use as the loss function can be used as follows:



      >>> y_pred = model.predict      #the prediction of the implemented model
      

      
>>> y_actu = data.target        #data labels
      

      
>>> cm = ConfusionMatrix(y_actu, y_pred)
      

      
>>> loss = cm.Kappa             #or any other parameter (Example: cm.SOA1)





      share|improve this answer











      $endgroup$
















        1












        1








        1





        $begingroup$

        If you can change the Loss function of the algorithm, It will be very helpful and as a result you don't need to down sample your data. There are many useful metrics which were introduced for evaluating the performance of classification methods for imbalanced data-sets. Some of them are Kappa, CEN, MCEN, MCC, and DP.



        Disclaimer:



        If you use python, PyCM module can help you to find out these metrics.



        Here is a simple code to get the recommended parameters from this module:



        >>> from pycm import *
        

        
>>> cm = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":2}, "Class2": {"Class1": 0, "Class2": 5}})  
        

        
>>> print(cm.recommended_list)
        
["Kappa", "SOA1(Landis & Koch)", "SOA2(Fleiss)", "SOA3(Altman)", "SOA4(Cicchetti)", "CEN", "MCEN", "MCC", "J", "Overall J", "Overall MCC", "Overall CEN", "Overall MCEN", "AUC", "AUCI", "G", "DP", "DPI", "GI"]


        After that, each of these parameters you want to use as the loss function can be used as follows:



        >>> y_pred = model.predict      #the prediction of the implemented model
        

        
>>> y_actu = data.target        #data labels
        

        
>>> cm = ConfusionMatrix(y_actu, y_pred)
        

        
>>> loss = cm.Kappa             #or any other parameter (Example: cm.SOA1)





        share|improve this answer











        $endgroup$



        If you can change the Loss function of the algorithm, It will be very helpful and as a result you don't need to down sample your data. There are many useful metrics which were introduced for evaluating the performance of classification methods for imbalanced data-sets. Some of them are Kappa, CEN, MCEN, MCC, and DP.



        Disclaimer:



        If you use python, PyCM module can help you to find out these metrics.



        Here is a simple code to get the recommended parameters from this module:



        >>> from pycm import *
        

        
>>> cm = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":2}, "Class2": {"Class1": 0, "Class2": 5}})  
        

        
>>> print(cm.recommended_list)
        
["Kappa", "SOA1(Landis & Koch)", "SOA2(Fleiss)", "SOA3(Altman)", "SOA4(Cicchetti)", "CEN", "MCEN", "MCC", "J", "Overall J", "Overall MCC", "Overall CEN", "Overall MCEN", "AUC", "AUCI", "G", "DP", "DPI", "GI"]


        After that, each of these parameters you want to use as the loss function can be used as follows:



        >>> y_pred = model.predict      #the prediction of the implemented model
        

        
>>> y_actu = data.target        #data labels
        

        
>>> cm = ConfusionMatrix(y_actu, y_pred)
        

        
>>> loss = cm.Kappa             #or any other parameter (Example: cm.SOA1)






        share|improve this answer














        share|improve this answer



        share|improve this answer








        edited yesterday

























        answered 2 days ago









        Alireza ZolanvariAlireza Zolanvari

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