Multivariate Stock Price Prediction Using a CEEMDAN–IGOA–TCN–iTransformer Hybrid Framework

Financial time series are characterized by high volatility, nonstationarity, nonlinear dynamics, and multiscale fluctuations, which pose significant challenges to accurate stock price forecasting. In multivariate financial forecasting, financial variables may exhibit symmetric and asymmetric characteristics in their temporal fluctuations and structural dependencies across different time scales. Understanding these characteristics is relevant to the analysis of symmetry and asymmetry in financial time series. Modeling multiscale temporal patterns and inter-variable dependencies is therefore important for improving forecasting performance. To address these challenges, this study proposes a hybrid CEEMDAN–IGOA–TCN–iTransformer framework for multivariate stock price prediction. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is employed to decompose financial time series into intrinsic mode functions and residual components, facilitating multiscale feature extraction. Second, a population-based hyperparameter optimization procedure is employed to select key model hyperparameters based on validation performance. Third, a TCN–iTransformer architecture is constructed, in which the Temporal Convolutional Network (TCN) extracts local temporal features and multiscale patterns, while the iTransformer models dependencies among financial input variables through its inverted attention mechanism. Finally, the extracted features are integrated to generate the predicted stock closing price. Experiments were conducted on three Chinese A-share stock datasets, representing Ping An Insurance, BOE Technology Group, and Arcplus Group. The proposed framework achieved test-set RMSE values of 0.8987, 0.0656, and 0.6717, respectively, with a corresponding arithmetic mean RMSE of 0.5453 across the three datasets. Comparative experiments and ablation studies were conducted to evaluate the forecasting performance and contributions of the framework’s components. The findings indicate that the proposed approach can provide accurate one-step-ahead closing-price predictions under the investigated experimental conditions, while its performance and generalizability remain dependent on the characteristics of the evaluated datasets.