Hi Yeray,
Developing and training Transformers for time series data, such as S&P 500 historical data, is indeed possible. While Transformers are widely used in NLP tasks, they can also be applied to other domains, including time series analysis.
To get started, you can take a look at the following tips:
- Familiarize yourself with the basics of Transformers and their architecture. Understanding concepts like self-attention, multi-head attention, and positional encoding will be helpful.
- Explore the MATLAB package you found Transformer Models. Although it may be more focused on language tasks, you can still adapt it for time series analysis.
- Preprocess your S&P 500 historical data into a suitable format for training the Transformer model. You may need to consider factors such as input window size, target prediction horizon, and normalization.
- Train the Transformer model using your preprocessed data by adjusting the hyperparameters such as learning rate, batch size, and number of layers, to achieve optimal performance.
- Evaluate the trained model's performance on validation or test data using appropriate metrics for time series analysis, such as mean squared error (MSE) or mean absolute error (MAE).
- Remember that developing and training Transformers for time series analysis may require some experimentation and fine-tuning to achieve the desired results.
Hope you find it helpful. Good luck with your research!
