Metaheuristic Approaches to Feature Engineering and Model Architecture Optimization for Financial Time Series Prediction

Predictive modeling of financial data, especially trading activity or asset prices, is a very challenging task. There are a number of novel approaches to feature engineering, data preparation and model architectures that aim to mitigate some of the problems that arise from non-stationarity and other issues typically found in financial time series data. This project aims to use metaheuristic approaches to approximate the best combination of feature engineering, data processing and model architectures for a specific financial time series prediction problem, while reducing the overall computation and time required for a grid search.

Éric Pfleiderer
Superviseur universitaire: 
Fabian Bastin
Partner University: