Multivaritate time series with keras LSTM for multiple groups












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$begingroup$


I am trying to solve a time-series problem using keras seq-to-seq architecture where I have data available for multiple cities. All examples I have seen online (e.g. Beijing weather dataset) have only one city. But I have around 800 cities.



My Dataset:



several cities
for which I have 25 features say temperature, humidity, air pressure, geo-location, elevation, wind speed etc.



samples for 2 years (one record for each day)



what I want to predict:
I want to predict the temperature for next 7 days for each city.



What I am confused about: I know I can shape my input features to 3-dimension like (batchsize, timesteps, features), so if I want to take the last 30 days weather data to predict next 7 values, I can to (?, 30, 25) and the output would be (?, 7, 1)



but how do I do it for multiple cities? I can create one model for each city but that would not be ideal solution (having 1000+ models).



I want to know the correct way to do it? can I create a 3 dimensional array separately for each city and then concatenate them together to feed it into one model?










share|improve this question









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    0












    $begingroup$


    I am trying to solve a time-series problem using keras seq-to-seq architecture where I have data available for multiple cities. All examples I have seen online (e.g. Beijing weather dataset) have only one city. But I have around 800 cities.



    My Dataset:



    several cities
    for which I have 25 features say temperature, humidity, air pressure, geo-location, elevation, wind speed etc.



    samples for 2 years (one record for each day)



    what I want to predict:
    I want to predict the temperature for next 7 days for each city.



    What I am confused about: I know I can shape my input features to 3-dimension like (batchsize, timesteps, features), so if I want to take the last 30 days weather data to predict next 7 values, I can to (?, 30, 25) and the output would be (?, 7, 1)



    but how do I do it for multiple cities? I can create one model for each city but that would not be ideal solution (having 1000+ models).



    I want to know the correct way to do it? can I create a 3 dimensional array separately for each city and then concatenate them together to feed it into one model?










    share|improve this question









    $endgroup$















      0












      0








      0





      $begingroup$


      I am trying to solve a time-series problem using keras seq-to-seq architecture where I have data available for multiple cities. All examples I have seen online (e.g. Beijing weather dataset) have only one city. But I have around 800 cities.



      My Dataset:



      several cities
      for which I have 25 features say temperature, humidity, air pressure, geo-location, elevation, wind speed etc.



      samples for 2 years (one record for each day)



      what I want to predict:
      I want to predict the temperature for next 7 days for each city.



      What I am confused about: I know I can shape my input features to 3-dimension like (batchsize, timesteps, features), so if I want to take the last 30 days weather data to predict next 7 values, I can to (?, 30, 25) and the output would be (?, 7, 1)



      but how do I do it for multiple cities? I can create one model for each city but that would not be ideal solution (having 1000+ models).



      I want to know the correct way to do it? can I create a 3 dimensional array separately for each city and then concatenate them together to feed it into one model?










      share|improve this question









      $endgroup$




      I am trying to solve a time-series problem using keras seq-to-seq architecture where I have data available for multiple cities. All examples I have seen online (e.g. Beijing weather dataset) have only one city. But I have around 800 cities.



      My Dataset:



      several cities
      for which I have 25 features say temperature, humidity, air pressure, geo-location, elevation, wind speed etc.



      samples for 2 years (one record for each day)



      what I want to predict:
      I want to predict the temperature for next 7 days for each city.



      What I am confused about: I know I can shape my input features to 3-dimension like (batchsize, timesteps, features), so if I want to take the last 30 days weather data to predict next 7 values, I can to (?, 30, 25) and the output would be (?, 7, 1)



      but how do I do it for multiple cities? I can create one model for each city but that would not be ideal solution (having 1000+ models).



      I want to know the correct way to do it? can I create a 3 dimensional array separately for each city and then concatenate them together to feed it into one model?







      deep-learning keras sequence






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      asked Apr 11 '18 at 9:06









      OshoOsho

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              push, I have a similar issue, someone has a suggestion?
              Thanks!





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                push, I have a similar issue, someone has a suggestion?
                Thanks!





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                push, I have a similar issue, someone has a suggestion?
                Thanks!






                share








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                Jorge 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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                answered 8 mins ago









                JorgeJorge

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