Journal of Forecasting

Abstract

Stock price time-series presents inherent difficulties due to their nearly random-walk behavior characterized by low signal-tonoise ratios and frequent regime shifts, whereas distinct industries add further complexity through unique volatility profiles and driving factors, which render cross-sector generalization especially demanding for predictive models.This study introduces IntraFormer, a novel Transformer architecture designed specifically for multivariate stock price prediction, integrating specialized intra-series attention mechanisms that enforce strict temporal causality, prioritize short-range dependencies, and incorporate gated residuals to combat noise overfitting while enhancing robustness against non-stationarity.Comprehensive experiments on 30 stocks spanning technology, healthcare, and financial sectors demonstrate IntraFormer's clear dominance: it consistently delivers the lowest MAE and RMSE, reducing them by 70%-90% compared with state-of-the-art Transformer variants, whose MAPE often exceeds 60%-99% in volatile cases; relative to recurrent baselines, IntraFormer achieves 20%-60% lower errors overall, with 30%-50% MAPE improvements.IntraFormer's portoflio construction returns nearly 11% on investment.This work establishes IntraFormer as a generalizable framework, offering potential for improving accuracy in trading strategies and portoflio risk managment across diverse market conditions.

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