To address challenges in stock price prediction including data nonlinearity and anomalies, we propose a hybrid CNN-BiGRU-AM framework integrated with deep learning-based anomaly detection. First, an anomaly detection module identifies irregularities in stock price data. The CNN component then extracts local features while filtering anomalous information, followed by nonlinear pattern modeling through BiGRU with attention mechanisms. Final predictions undergo secondary anomaly screening to ensure reliability. Experimental evaluation on Shanghai Composite (SSE) daily closing prices demonstrates superior performance with $R^2$ = 0.9903, RMSE = 22.027, MAE = 19.043, and a Sharpe Ratio of 0.65. It is noteworthy that the MAE of this model is reduced by 14.7%, and the RMSE is decreased by 7.7% compared to its ablation model. The framework achieves multi-level feature extraction through convolutional operations and bidirectional temporal modeling, effectively enhancing model generalization via nonlinear mapping and anomaly correction. Comparative Sharpe Ratio analysis across models provides practical insights for investment decision-making. This dual-functional system not only improves prediction accuracy but also offers interpretable references for market mechanism analysis and regulatory policy formulation.

This paper introduces an innovative rapid algorithm for estimating two-dimensional direction of arrival (2D-DOA) with randomly arranged electromagnetic vector sensor (EMVS) arrays under nonuniform noise conditions. The approach begins by forming the covariance matrix of the received signal matrix, after which elements affected by noise are removed based on the characteristics of nonuniform noise to reduce its disruptive effects. Subsequently, the pure covariance matrix is filled using a matrix completion algorithm and then reconstructed into a new matrix. Finally, the signal subspace is extracted by the propagator method (PM) algorithm, and the 2D-DOA is estimated via a method analogous to the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT). Theoretical analyses confirm the high degrees of freedom of the algorithm, low computational complexity, and accuracy of the estimation. Simulation results validate that the proposed algorithm exhibits remarkable resilience against nonuniform noise. When compared with conventional algorithms such as ESPRIT, ESPRIT-like, and improved ESPRIT (IESPRIT), it also shows better performance in terms of estimation speed and accuracy.

This paper addresses the two-dimensional direction-of-arrival(2D-DOA) estimation problem using an Electromagnetic Vector Sensor (EMVS) array, where an arbitrary array geometry and the presence of unknown nonuniform noise are considered. A novel algorithm combining tensor completion and Parallel Factor Decomposition (PARAFAC) is proposed to improve the accuracy of estimation. Firstly, a covariance tensor is constructed. The impact of non-uniform noise is eliminated via removing the noise-affected elements of the covariance tensor. The noise-free covariance matrix is then recovered through tensor completion, and the target angles are extracted using PARAFAC algorithm. Additionally, Cramer-Rao Bounds (CRB) for joint 2D-DOA and polarization parameter estimation are derived, providing a theoretical basis for array design. Simulation results demonstrate that the proposed algorithm is robust to nonuniform noise and achieves high-resolution 2D-DOA estimation, outperforming traditional methods in terms of accuracy. Comparative evaluations confirm the superiority of the proposed geometry and denoising approach.