2 years ago

#65308

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matthew_obrien

Recurrent Neural Models with Multiple Individuals/Ids

I'm fairly beginner in the world of machine learning and am trying to make some predictions on fantasy sport performance in baseball on any given night. Given the sequential nature of the data, recurrent neural networks seemed liked a good starting point.

I understand the basic principles of rnn but what isn't clear to mean is how to incorporate multiple time series' from different individuals into a single model. For instance, we have performance for 2000 players across each of their career and hence have 2000 distinct time series. In order to make use of rnn, would I have to build models for each player separately, or is it possible/better to pass a player's ID into the model as a feature?

If the latter is possible, I'm still unsure about how this would mechanically work, because players have different time series lengths, and we would have many time series observations for a particular point in time.

Some references/examples/advice would be very helpful.

tensorflow

keras

deep-learning

lstm

recurrent-neural-network

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