Research on deep learning model for stock prediction by integrating frequency domain and time series features
Dataset introduction
This study selects historical trading data from the NASDAQ and NYSE between January 2013 and August 2017, including five core features (open, high, low, close, and volume). A sliding window mechanism with a lookback of 16 days is used to generate sequences, ensuring local pattern capture while allowing the Transformer-based architecture to learn long-term dependencies. While the current implementation utilizes chunking and normalization procedures to prepare the input sequences, real-world stock datasets often contain missing entries, outliers, and abrupt changes caused by unexpected market shocks. To improve the model’s robustness and applicability in practical financial environments, we plan to incorporate…
