Tree-structured Parzen Estimator (TPE) Sampler, employs a probabilistic model to specify the most promising parameter configuration, navigating the search space while balancing exploration and exploitation within a complex and high-dimensional domain . Training unfolds over several epochs, with each iteration starting with a reset of the model's hidden states and zeroing gradients to ensure clean computation for the forward pass. The pinball loss function is selected for its effectiveness in probabilistic forecasting, eliminating the need for a presumptive data distribution model 37 unlike traditional metrics , which are more sensitive to noise and anomalies. These asymmetric and non-parametric criteria assess forecast accuracy by penalizing deviations from three targeted quantiles, namely 0.1, 0.5, and 0.9, enhancing robustness to outliers and the efficacy for LSTM-based networks . At the same time, a masking technique