Multivaritate time series with keras LSTM for multiple groups
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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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$begingroup$
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?
deep-learning keras sequence
$endgroup$
add a comment |
$begingroup$
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?
deep-learning keras sequence
$endgroup$
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
deep-learning keras sequence
asked Apr 11 '18 at 9:06
OshoOsho
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push, I have a similar issue, someone has a suggestion?
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push, I have a similar issue, someone has a suggestion?
Thanks!
New contributor
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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push, I have a similar issue, someone has a suggestion?
Thanks!
New contributor
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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push, I have a similar issue, someone has a suggestion?
Thanks!
New contributor
Jorge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Jorge is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 8 mins ago
JorgeJorge
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