Penalized estimation methods for diffusion processes and dependent data have recently gained significant attention due to their effectiveness in handling stochastic systems. In this work, we introduce an adaptive Elastic-Net estimator for ergodic diffusion processes observed under high-frequency sampling schemes. Our method combines the least squares approximation of the quasi-likelihood with adaptive \(\ell _1\) and \(\ell _2\) regularization. This approach allows to enhance prediction accuracy and interpretability while effectively recovering the sparse underlying structure of the model. In the spirit of recent research trends, we provide finite-sample guarantees for the (block-diagonal) estimator’s performance by deriving high-probability non-asymptotic bounds for the \(\ell _2\) estimation error. These results complement the established oracle properties in the high-frequency asymptotic regime with mixed convergence rates, ensuring consistent selection of the relevant interactions and achieving optimal rates of convergence. Furthermore, we utilize our results to analyze one-step-ahead predictions, offering non-asymptotic control over the \(\ell _1\) prediction error. The performance of our method is evaluated through simulations and real data applications, demonstrating its effectiveness, particularly in scenarios with strongly correlated variables.
