Selection of significant predictors from a large number of time series

Selection of significant predictors from a large number of time series

event_note 28.05.2021

Undervalued and overvalued stocks in our model are currently calculated as part of a proof of concept solution that is based on proprietary software with inner limitations.

Therefore, a new robust production system is being prepared. In this way, we can take advantage of synergies with existing components from other areas, for example for postprocessing of results.

These steps successfully completed the Feature selection section.

 

*The most demanding computational phase in creating a model is data preparation / preprocessing.

**The model could achieve great results when using training data (ie data that he saw during the learning process), but it could achieve poor results when using test data (that it did not see during the learning process).

***Under the term time series, you can imagine a change in the values ​​of indicators over time, such as close price or (not only from it) derived indicators such as Currency Volume. For these indicators, we always determine their significance to the target variable (for example, normalized daily yield) using various statistical tests (for example, the T-test).  To prevent overfitting, we remove insignificant time series from the model.