deeplearning4j-examples

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I have question about learning using time series data with infinite stream.

If I had set of historical data and then I want prediction for one step at the time and still improve my network should code look like this:

myModel.fit(historicalData)

// assuming newFeatures is infinite stream, for just represents futher steps
for(INDArray feature: newFeatures){
INDArray timeSeriesOutput = myNetwork.output(feature);

if(iCanGetLabelsForNewData){
// if I want predict labels for n step must wait n steps until I have label for new data
myModel.output(featureWithLabel - n, true);
}
}

Is my aproach correct?

Second problem: how to normalize data with infinite stream when values of features can be much higher than in historical data?

Have you tried our gitter channel for general questions like this one?

https://gitter.im/deeplearning4j/deeplearning4j

@KrzychuJedi - did you find out how to do this? I am also interested in achieving this. Thanks

@magick93 No, I didn't solve this problem.

PUt constant that is to be al w a y s applied to any data pull When new data t i c k comes update the constant