Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction
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View abstract on PubMed
Summary
This summary is machine-generated.Galformer, a novel transformer model, enhances multi-step stock market index prediction. It features a generative decoder for faster long sequence forecasting and a hybrid loss function for improved accuracy.
Area Of Science
- Financial forecasting
- Artificial intelligence in finance
- Time series analysis
Background
- Accurate stock market prediction is vital for financial decision-making.
- Deep learning, especially Transformer models, shows promise for stock index prediction.
- Existing Transformer models struggle with multi-step forecasting speed and capturing noisy financial data.
Purpose Of The Study
- To introduce Galformer, an innovative Transformer-based model for multi-step stock market index prediction.
- To address limitations in Transformer model inference speed and loss function suitability for financial data.
- To improve the accuracy and efficiency of stock market forecasting.
Main Methods
- Developed Galformer, a Transformer model with a generative decoder for single-forward, long-sequence prediction.
- Implemented a novel hybrid loss function combining quantitative error and trend accuracy.
- Evaluated performance on CSI 300, S&P 500, Dow Jones Industrial Average (DJI), and Nasdaq Composite (IXIC) indices.
Main Results
- Galformer demonstrated superior performance compared to classical methods on multiple stock market indices.
- The generative decoder significantly improved inference speed for long sequences.
- The hybrid loss function effectively optimized the model for noisy, nonlinear stock data.
Conclusions
- Galformer offers an effective solution for multi-step stock market index forecasting.
- The model's architecture and loss function enhance both speed and accuracy.
- Galformer represents a significant advancement in applying Transformer models to financial prediction.
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