This work is a data-driven, semi-automatic framework for inference of phenomenological models in complex systems, specifically climate. It is based on the Lasso multivariate regression model and is designed to quantify the compound affect that the complex interplay among variables in various temporal phases. This work, implemented in MATLAB, involves the development of the complete pipeline, starting with data preprocessing, an optimized Lasso implementation, a ranking module for active phase determination, a significance estimation module, and an impact analysis to infer causal relationships.