Learning using time series data with infinite stream - continuous learning and normalization

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?