- #1
BRN
- 108
- 10
Hi everyone,
I apologize to the mod if I posted in the wrong section.
For my exam of Machine Learning, I would like to implement a part of the work presented in this paper. In this work, the authors used two ML methods in cascade for forecasting Bitcoin. Starting from the initial data, they predicted the five main BTC indicators via SVR (Support Vector Regression) and the latter were then the inputs used to predict the price of Bitcoin via LSTMNN (Long Short Term Memory Neural Networks).
There are two things that I don't understand:
Can anyone clarify my ideas?
Thanks so much!
I apologize to the mod if I posted in the wrong section.
For my exam of Machine Learning, I would like to implement a part of the work presented in this paper. In this work, the authors used two ML methods in cascade for forecasting Bitcoin. Starting from the initial data, they predicted the five main BTC indicators via SVR (Support Vector Regression) and the latter were then the inputs used to predict the price of Bitcoin via LSTMNN (Long Short Term Memory Neural Networks).
There are two things that I don't understand:
- Starting with a dataset of about 2600 rows, 80% of them are used for Trainig and only 20% for the prediction coming out of SVR. This data are ulteriorly separated by incoming in LSTMNN and follows that the prediction in this stage is made on a truly reduced sample compared to the starting one. It's not a problem?
- If the input to LSTMNN are only the indicator predicted by SVR, how is it possible to forecasting the price of the BTCs? At this stage there is only the X matrix of the indicators, but there is not price vector...
Can anyone clarify my ideas?
Thanks so much!