CODEWITHSUNDEEP
neural-networkkerasdeep-learninglstmrecurrent-neural-network
BigBadMe
BigBadMe

Reputation: 1842

Multivariate and multi-series LSTM

I am trying to create a pollution prediction LSTM. I've seen an example on the web to cater for a Multivariate LSTM to predict the pollution levels for one city (Beijing), but what about more than one city? I don't really want a separate network for every city, I'd like a single generalised model/network for all x cities. But how do I feed that data into the LSTM?

Say I have the same data for each city, do I...

1) Train on all data for one city, then the next city, and so on until all cities are done.

2) Train data for all cities on date t, then data for all cities on t+1, then t+2 etc.

3) Something completely different.

Any thoughts?

Upvotes: 2

Views: 367

Answers (1)

Vadim
Vadim

Reputation: 4519

I would try first the (1).

Also, you can try a multi inputs / multi outputs network. I mean you have 10 cities. Therefore, your network would have 10 RNN inputs and 10 outputs.

Here is a great tutorial on how to do it with Keras: https://keras.io/getting-started/functional-api-guide/

I'm not sure if it will work, but you can give it a try.

Upvotes: 1

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