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# Gradient booster versus neural net

hi,

I'm concluding an exercise around time series. So far I have explored different ways varying from naive models, STL decomposition, Holt-Winters, Arima which are time-series models. I would like to explore real machine learning models. I have seen a RapidMiner tutorial in relation to windowing which is applying a gradient booster.

1- How does this work precisely?

2- What is the difference with e.g. a neural net model?

3- How are such ML models different than the

I copy @mschmitz as reference following our recent discussion.

Thank you beforehand,

Bart

I'm concluding an exercise around time series. So far I have explored different ways varying from naive models, STL decomposition, Holt-Winters, Arima which are time-series models. I would like to explore real machine learning models. I have seen a RapidMiner tutorial in relation to windowing which is applying a gradient booster.

1- How does this work precisely?

2- What is the difference with e.g. a neural net model?

3- How are such ML models different than the

*regular*time-series models?I copy @mschmitz as reference following our recent discussion.

Thank you beforehand,

Bart

0

## Answers

391Unicorn38Contributor I3 Questions, sir- that occur to me off of your excellent post:- re the
- per the

- can these approaches be combined somehow and, if so, can you show a newbie like myself
- lastly, do we internally have the options to work with either

thank you & good morning!! - RichardCNNapproach is there an operator that I can look at and experiment with for this? what is it called?LSTMapproach same question exactly.a simple diagramabout how that might look?RL or GRUapproaches?391Unicorn38Contributor I391Unicorn38Contributor I