Algorithms

The Fusion Modules utilize various algorithms and methods to optimize results:

1. Weighted Averaging

• Where:

wi: Weight of DL Module i.

Output: Output from DL Module i.

1. Example

If DL Module 1 predicts a 5% market increase (weight 0.6) and DL Module 2 predicts a 3% increase (weight 0.4), the Fusion Modules will combine them as follows:

1. Voting Mechanism

• When modules provide different predictions, the Fusion Module selects the result with the highest "consensus." For example:

• DL Module 1 and DL Module 3 predict an increase in stock price.

• DL Module 2 predicts a decrease in price.

• → The Fusion Module will select the "increase" prediction because it has a 2/3 module consensus.

1. Stacking models

• A meta-model learns how to combine the outputs from the DL Modules. This meta-model can be a simple machine learning model like logistic regression or a more complex one like gradient boosting.

Example:

• The meta-model learns that during stable market periods, DL Module 1 has higher reliability, but when the market is volatile, DL Module 2 is more reliable.