As far as I know this is still active research. You have research showing recurrent networks being superior to feed-forward networks for time series forecasting?
Recurrent neural networks are superior for modelling cognitive processes. Since recurrence is proven to be an important part of the workings of our brain.
If you plan to do research comparing feed-forward networks and recurrent networks, I would strongly recommend to create an own implementation.
Recurrent NNs are quite mature already. They are several decades old. They are like a standard tool to model dynamical systems.
Feedforward NNs can't extrapolate time series data well. They can only interpolate/do curve-fitting. You need recurrent NNs to predict time series generated by dyanamical systems
Recurrent NNs are quite mature already. They are several decades old. They are like a standard tool to model dynamical systems.
For modelling cognitive processes yes.
prolog wrote:
Feedforward NNs can't extrapolate time series data well. They can only interpolate/do curve-fitting. You need recurrent NNs to predict time series generated by dyanamical systems
Not to this moment but since Tensor Flow has an Java API now, maybe some intrepid Java developer could take a stab it and build a Tensor Flow extension.
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Answers
Feed-forward NNs can't handle time series prediction well.
You have research showing recurrent networks being superior to feed-forward networks for time series forecasting?
Recurrent neural networks are superior for modelling cognitive processes.
Since recurrence is proven to be an important part of the workings of our brain.
If you plan to do research comparing feed-forward networks and recurrent networks, I would strongly recommend to create an own implementation.
They are several decades old.
They are like a standard tool to model dynamical systems.
Feedforward NNs can't extrapolate time series data well. They can only interpolate/do curve-fitting.
You need recurrent NNs to predict time series generated by dyanamical systems
Show me the proof? :P
This conversation is already five years old, and there's still no RNN's in Rapid Miner ? I googled it and this was the first thing to come up ...
RNN is not stock in RapidMiner at this very moment. That said we have the DeepLearning4j extension available from our partner, you can check it out here: https://www.rapidminerchina.com/en/products/shop/product/deeplearning4j/
Are there any plans to implement it native- incl. LSTMs?
Love you all.
Not to this moment but since Tensor Flow has an Java API now, maybe some intrepid Java developer could take a stab it and build a Tensor Flow extension.