The challenge of time series forecasting has been the focus of research in recent years, with Transformer-based models using various self-attention mechanisms to uncover longrange dependencies. However, complex trends and nonlinear serial dependencies presented in some specific datasets may not always be captured properly. To address these issues, we present STLformer, a novel Transformer-based model that utilizes an STL decomposition architecture and the rank correlation function to improve long-term time series forecasting. STLformer outperforms four state-of-the-art Transformers and two RNN models across multiple datasets and forecasting horizons.