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Training a LSTM on a time serie containing multiple inputs for each timestep



2019 Community Moderator ElectionTime series prediction using ARIMA vs LSTMLSTM with multiple entries per time stepLSTM - How to prepare train from a dataset which contains multiple observations for different eventsHow to learn from time series with multiple values for each time pointsForecasting vs non-forecasting predition for time series anomaly detectionMulti-Step Forecast for Multivariate Time Series (LSTM) KerasIs there an R tutorial of using LSTM for multivariate time series forecasting?Train LSTM model with multiple time seriesHow to reshape data for LSTM training in multivariate sequence predictionLSTM Time series prediction for multiple multivariate series










0












$begingroup$


I am trying to train a LSTM in order to use it for forecasting : the problem is basically a multivariate multi-steps time series problem.



It is simply an experiment to see how statistical models (ARIMA, Holts-Winters, ...) and neural networks compare for a given problem.



As my dataset is perfectly fit for a statistical model, I am having trouble when trying to format it to train the LSTM as I have multiple entries for one timestep (corresponding to different entities) and I don't really know how to deal with it since the sequence is no longer tied by the time of observation. Let's say my dataset looks like the following example :



time | ent | obs



1 --- 1 ------ 5



2 --- 1 ------ 6



2 --- 5 ------ 1



3 --- 2 ------ 7



3 --- 5 ------ 4



As you can see, not every entity have an entry for any given time, and one timestep can have multiple entries.



I thought of training the LSTM for each entity but I would have too few data for most of them. Some threads gave me the idea to separate each entity into batches but the number of observations is not constant so it wouldn't work for me.



How do you think I am supposed to tackle this problem ?










share|improve this question







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    0












    $begingroup$


    I am trying to train a LSTM in order to use it for forecasting : the problem is basically a multivariate multi-steps time series problem.



    It is simply an experiment to see how statistical models (ARIMA, Holts-Winters, ...) and neural networks compare for a given problem.



    As my dataset is perfectly fit for a statistical model, I am having trouble when trying to format it to train the LSTM as I have multiple entries for one timestep (corresponding to different entities) and I don't really know how to deal with it since the sequence is no longer tied by the time of observation. Let's say my dataset looks like the following example :



    time | ent | obs



    1 --- 1 ------ 5



    2 --- 1 ------ 6



    2 --- 5 ------ 1



    3 --- 2 ------ 7



    3 --- 5 ------ 4



    As you can see, not every entity have an entry for any given time, and one timestep can have multiple entries.



    I thought of training the LSTM for each entity but I would have too few data for most of them. Some threads gave me the idea to separate each entity into batches but the number of observations is not constant so it wouldn't work for me.



    How do you think I am supposed to tackle this problem ?










    share|improve this question







    New contributor




    naifmeh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      0












      0








      0





      $begingroup$


      I am trying to train a LSTM in order to use it for forecasting : the problem is basically a multivariate multi-steps time series problem.



      It is simply an experiment to see how statistical models (ARIMA, Holts-Winters, ...) and neural networks compare for a given problem.



      As my dataset is perfectly fit for a statistical model, I am having trouble when trying to format it to train the LSTM as I have multiple entries for one timestep (corresponding to different entities) and I don't really know how to deal with it since the sequence is no longer tied by the time of observation. Let's say my dataset looks like the following example :



      time | ent | obs



      1 --- 1 ------ 5



      2 --- 1 ------ 6



      2 --- 5 ------ 1



      3 --- 2 ------ 7



      3 --- 5 ------ 4



      As you can see, not every entity have an entry for any given time, and one timestep can have multiple entries.



      I thought of training the LSTM for each entity but I would have too few data for most of them. Some threads gave me the idea to separate each entity into batches but the number of observations is not constant so it wouldn't work for me.



      How do you think I am supposed to tackle this problem ?










      share|improve this question







      New contributor




      naifmeh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I am trying to train a LSTM in order to use it for forecasting : the problem is basically a multivariate multi-steps time series problem.



      It is simply an experiment to see how statistical models (ARIMA, Holts-Winters, ...) and neural networks compare for a given problem.



      As my dataset is perfectly fit for a statistical model, I am having trouble when trying to format it to train the LSTM as I have multiple entries for one timestep (corresponding to different entities) and I don't really know how to deal with it since the sequence is no longer tied by the time of observation. Let's say my dataset looks like the following example :



      time | ent | obs



      1 --- 1 ------ 5



      2 --- 1 ------ 6



      2 --- 5 ------ 1



      3 --- 2 ------ 7



      3 --- 5 ------ 4



      As you can see, not every entity have an entry for any given time, and one timestep can have multiple entries.



      I thought of training the LSTM for each entity but I would have too few data for most of them. Some threads gave me the idea to separate each entity into batches but the number of observations is not constant so it wouldn't work for me.



      How do you think I am supposed to tackle this problem ?







      time-series lstm preprocessing forecasting






      share|improve this question







      New contributor




      naifmeh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      naifmeh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question






      New contributor




      naifmeh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked 4 hours ago









      naifmehnaifmeh

      11




      11




      New contributor




      naifmeh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.





      New contributor





      naifmeh is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






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      Check out our Code of Conduct.




















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          $begingroup$

          The answer to this question highly depends on what relationship between the variables you are interested in.



          • If you are interested in the relationship between time and observation-value, treating the entities as different batches could make sense, under the assumption that the role of individual entities doesn't really matter to you. In this case, you would, for example, add the mean of each entity (or the overall mean) to all entities with missing values to get a constant number of observations per entity. But you could also simply average all values in each timestamp and include other features as min & max. This would most probably deliver better results.


          • If you are interested in the relationship between entities and observation-value, this is a matter of missing data in time series. There are a lot of techniques that can help you with that from simply imputing the mean to more sophisticated methods like a Kalman filter. However, in the end, you will have to ask yourself why these observations are missing and choose the appropriate method. But since you are using time-dependent models in your experiment, I assume, this is not of interest to you.


          • If you are interested in the interrelationship of all three variables, you are dealing with panel data. In this case, I don't see a reasonable possibility to model this with an LSTM. Maybe another RNN-architecture could work, however, the only paper I found was Tensorial Recurrent Neural Networks for Longitudinal Data Analysis from Mingyuan et.al. But in the end, it would not matter, since an ARIMA-model also isn't appropriate for panel data. Usually, you use a Difference-In-Differences approach for that kind of data. In this case, I would suggest changing the dataset for your experiment.






          share|improve this answer











          $endgroup$













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            0












            $begingroup$

            The answer to this question highly depends on what relationship between the variables you are interested in.



            • If you are interested in the relationship between time and observation-value, treating the entities as different batches could make sense, under the assumption that the role of individual entities doesn't really matter to you. In this case, you would, for example, add the mean of each entity (or the overall mean) to all entities with missing values to get a constant number of observations per entity. But you could also simply average all values in each timestamp and include other features as min & max. This would most probably deliver better results.


            • If you are interested in the relationship between entities and observation-value, this is a matter of missing data in time series. There are a lot of techniques that can help you with that from simply imputing the mean to more sophisticated methods like a Kalman filter. However, in the end, you will have to ask yourself why these observations are missing and choose the appropriate method. But since you are using time-dependent models in your experiment, I assume, this is not of interest to you.


            • If you are interested in the interrelationship of all three variables, you are dealing with panel data. In this case, I don't see a reasonable possibility to model this with an LSTM. Maybe another RNN-architecture could work, however, the only paper I found was Tensorial Recurrent Neural Networks for Longitudinal Data Analysis from Mingyuan et.al. But in the end, it would not matter, since an ARIMA-model also isn't appropriate for panel data. Usually, you use a Difference-In-Differences approach for that kind of data. In this case, I would suggest changing the dataset for your experiment.






            share|improve this answer











            $endgroup$

















              0












              $begingroup$

              The answer to this question highly depends on what relationship between the variables you are interested in.



              • If you are interested in the relationship between time and observation-value, treating the entities as different batches could make sense, under the assumption that the role of individual entities doesn't really matter to you. In this case, you would, for example, add the mean of each entity (or the overall mean) to all entities with missing values to get a constant number of observations per entity. But you could also simply average all values in each timestamp and include other features as min & max. This would most probably deliver better results.


              • If you are interested in the relationship between entities and observation-value, this is a matter of missing data in time series. There are a lot of techniques that can help you with that from simply imputing the mean to more sophisticated methods like a Kalman filter. However, in the end, you will have to ask yourself why these observations are missing and choose the appropriate method. But since you are using time-dependent models in your experiment, I assume, this is not of interest to you.


              • If you are interested in the interrelationship of all three variables, you are dealing with panel data. In this case, I don't see a reasonable possibility to model this with an LSTM. Maybe another RNN-architecture could work, however, the only paper I found was Tensorial Recurrent Neural Networks for Longitudinal Data Analysis from Mingyuan et.al. But in the end, it would not matter, since an ARIMA-model also isn't appropriate for panel data. Usually, you use a Difference-In-Differences approach for that kind of data. In this case, I would suggest changing the dataset for your experiment.






              share|improve this answer











              $endgroup$















                0












                0








                0





                $begingroup$

                The answer to this question highly depends on what relationship between the variables you are interested in.



                • If you are interested in the relationship between time and observation-value, treating the entities as different batches could make sense, under the assumption that the role of individual entities doesn't really matter to you. In this case, you would, for example, add the mean of each entity (or the overall mean) to all entities with missing values to get a constant number of observations per entity. But you could also simply average all values in each timestamp and include other features as min & max. This would most probably deliver better results.


                • If you are interested in the relationship between entities and observation-value, this is a matter of missing data in time series. There are a lot of techniques that can help you with that from simply imputing the mean to more sophisticated methods like a Kalman filter. However, in the end, you will have to ask yourself why these observations are missing and choose the appropriate method. But since you are using time-dependent models in your experiment, I assume, this is not of interest to you.


                • If you are interested in the interrelationship of all three variables, you are dealing with panel data. In this case, I don't see a reasonable possibility to model this with an LSTM. Maybe another RNN-architecture could work, however, the only paper I found was Tensorial Recurrent Neural Networks for Longitudinal Data Analysis from Mingyuan et.al. But in the end, it would not matter, since an ARIMA-model also isn't appropriate for panel data. Usually, you use a Difference-In-Differences approach for that kind of data. In this case, I would suggest changing the dataset for your experiment.






                share|improve this answer











                $endgroup$



                The answer to this question highly depends on what relationship between the variables you are interested in.



                • If you are interested in the relationship between time and observation-value, treating the entities as different batches could make sense, under the assumption that the role of individual entities doesn't really matter to you. In this case, you would, for example, add the mean of each entity (or the overall mean) to all entities with missing values to get a constant number of observations per entity. But you could also simply average all values in each timestamp and include other features as min & max. This would most probably deliver better results.


                • If you are interested in the relationship between entities and observation-value, this is a matter of missing data in time series. There are a lot of techniques that can help you with that from simply imputing the mean to more sophisticated methods like a Kalman filter. However, in the end, you will have to ask yourself why these observations are missing and choose the appropriate method. But since you are using time-dependent models in your experiment, I assume, this is not of interest to you.


                • If you are interested in the interrelationship of all three variables, you are dealing with panel data. In this case, I don't see a reasonable possibility to model this with an LSTM. Maybe another RNN-architecture could work, however, the only paper I found was Tensorial Recurrent Neural Networks for Longitudinal Data Analysis from Mingyuan et.al. But in the end, it would not matter, since an ARIMA-model also isn't appropriate for panel data. Usually, you use a Difference-In-Differences approach for that kind of data. In this case, I would suggest changing the dataset for your experiment.







                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited 1 hour ago

























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                georg_ungeorg_un

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                    ValueError: Expected n_neighbors <= n_samples, but n_samples = 1, n_neighbors = 6 (SMOTE) The 2019 Stack Overflow Developer Survey Results Are InCan SMOTE be applied over sequence of words (sentences)?ValueError when doing validation with random forestsSMOTE and multi class oversamplingLogic behind SMOTE-NC?ValueError: Error when checking target: expected dense_1 to have shape (7,) but got array with shape (1,)SmoteBoost: Should SMOTE be ran individually for each iteration/tree in the boosting?solving multi-class imbalance classification using smote and OSSUsing SMOTE for Synthetic Data generation to improve performance on unbalanced dataproblem of entry format for a simple model in KerasSVM SMOTE fit_resample() function runs forever with no result