Adaptive Weighted Regularized <scp>QRGRU</scp> Algorithm and Its Application in Stock Price Prediction

ABSTRACT In the era of big data, accurately predicting trends and uncertainties in time series data is crucial for various fields, such as finance and engineering. This paper proposes an adaptive weighted regularized quantile regression gated recurrent unit (AWR‐QRGRU) algorithm. The gated recurrent unit (GRU) is employed to capture long‐term dependencies in the sequence and generate predictions for multiple quantiles through a fully connected layer, thereby enabling both point forecasts and interval forecasts with varying confidence levels. In the proposed prediction algorithm, the loss function incorporates coverage loss, median loss, interval width loss, and regularization loss. An adaptive weight adjustment mechanism was implemented to dynamically optimize the weights of these different loss terms, enhancing the accuracy and stability of the predictions when dealing with multidimensional explanatory variables. Subsequently, we conducted Monte Carlo experiments to validate the algorithm's effectiveness in both point and interval predictions. Ultimately, the algorithm was applied to predict Amazon's and NVIDIA's stock prices, showcasing its potential applications in complex financial market environments.