Transformers vs. LSTMs for Time-Series Forecasting

For years, LSTMs have been a go-to architecture for time-series forecasting due to their ability to capture sequential dependencies. However, the Transformer architecture, with its self-attention mechanism, has shown remarkable success in NLP and is now making significant inroads into time-series analysis. This research paper presents a comparative study of Transformer-based models against traditional LSTMs on various financial time-series datasets, including stock prices and volatility indices.

Our findings indicate that while LSTMs perform well on simpler, more stationary series, Transformers excel at capturing complex, long-range dependencies and multi-variate interactions. We discuss the importance of positional encodings and the challenges of applying attention mechanisms to noisy financial data. The paper concludes that with proper tuning and feature engineering, Transformer models can offer a superior foundation for next-generation forecasting models in finance.