TL;DR
A prediction-explanation network called AgentPEN, which can provide clear explanations for complex temporal price patterns and surpasses the state-of-the-art baselines both in prediction accuracy and explainability.
The revised evidence supports lower price-level errors, while directional and significance results are mixed across markets, and the findings establish cross-market consistency rather than transfer learning.
Findings show that integration strategy and representation preservation—not the mere inclusion of attention—determine whether attention-augmented lightweight models train reliably.
Financial texts such as company-specific news and earnings reports contain useful signals about the future stock price movements of individual stocks, and their use in stock price prediction has gained growing attention. A common approach is to encode both financial text and historical price time series into embeddings...
Forecasting financial market movements requires integrating numerical time-series data with semantic information from financial narratives that shape investor behavior. While deep learning has advanced time-series prediction, existing methods often exploit trivial price autocorrelation rather than capturing genuine pre...
DAT-TimeXer is proposed, a structure-aware adaptation of TimeXer for closing-price forecasting that achieves the lowest mean forecasting errors among the compared models in the one-step evaluations and maintains lower errors at horizons of 1, 3, 5, and 10 in the evaluated multi-step tasks.
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