Longitudinal data, which is widely used in many disciplines to study cause and effect, poses significant computational challenges to both modeling and analysis. Longitudinal data is composed of readings on the same variable collected over time and is often high-dimensional with correlated features. The combinatorial search approach for identifying the optimal features is unrealistic for most applications. The alternative approaches, such as heuristics, greedy searches, and regularization techniques, including LASSO, can result in models that suffer from both low accuracy and unclear feature attribution. In this paper, we propose a binary transformation on the data before applying LASSO for feature learning. As demonstrated in the paper, the binary transformation enhances signal in the data, resulting in highly accurate feature attribution, including associated time lags. It avoids the typical shortcomings of the LASSO algorithm, including saturation of the feature space and arbitrary or inconsistent sparse feature selection. Both synthetic data and real-world data sets were used to demonstrate the value of the proposed transformation and in all cases substantial improvements in feature learning were seen. In addition, the scalable parallelism of the solution is superior to that of the standard LASSO since transformation itself occurs in linear time and computing the LASSO solution using the transformed data results in a speedup of almost double.