Python RNN for not uniformly timed sequences using keras












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I'm trying to build a model to predict timestamp and classification based on event based sequences ie a value only appears based on an event.



For instance:



[['apple','1-1-2019'], ['orange','2-1-2019'], ['banana','5-1-2019'], ['orange','10-1-2019']] ---> ['watermelon','12-1-2019']


Here my input sequence in non uniform (we cant assume that constant time has passed between 2 consecutive instances in the data set) and I want to predict label and its time of occurrence.



How can this be achieved with RNN LSTM?
Assuming I have 10 labels in total






python deep-learning keras lstm rnn







share|improve this question











$endgroup$

















    0












    $begingroup$


    I'm trying to build a model to predict timestamp and classification based on event based sequences ie a value only appears based on an event.



    For instance:



    [['apple','1-1-2019'], ['orange','2-1-2019'], ['banana','5-1-2019'], ['orange','10-1-2019']] ---> ['watermelon','12-1-2019']


    Here my input sequence in non uniform (we cant assume that constant time has passed between 2 consecutive instances in the data set) and I want to predict label and its time of occurrence.



    How can this be achieved with RNN LSTM?
    Assuming I have 10 labels in total






    python deep-learning keras lstm rnn







    share|improve this question











    $endgroup$















      0












      0








      0





      $begingroup$


      I'm trying to build a model to predict timestamp and classification based on event based sequences ie a value only appears based on an event.



      For instance:



      [['apple','1-1-2019'], ['orange','2-1-2019'], ['banana','5-1-2019'], ['orange','10-1-2019']] ---> ['watermelon','12-1-2019']


      Here my input sequence in non uniform (we cant assume that constant time has passed between 2 consecutive instances in the data set) and I want to predict label and its time of occurrence.



      How can this be achieved with RNN LSTM?
      Assuming I have 10 labels in total






      python deep-learning keras lstm rnn







      share|improve this question











      $endgroup$




      I'm trying to build a model to predict timestamp and classification based on event based sequences ie a value only appears based on an event.



      For instance:



      [['apple','1-1-2019'], ['orange','2-1-2019'], ['banana','5-1-2019'], ['orange','10-1-2019']] ---> ['watermelon','12-1-2019']


      Here my input sequence in non uniform (we cant assume that constant time has passed between 2 consecutive instances in the data set) and I want to predict label and its time of occurrence.



      How can this be achieved with RNN LSTM?
      Assuming I have 10 labels in total






      python deep-learning keras lstm rnn




      python deep-learning keras lstm rnn






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited 9 hours ago







      Ammar Ahmed

















      asked 13 hours ago









      Ammar AhmedAmmar Ahmed

      62




      62






















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