This paper introduces the Directional Time Attention Transformer (DTAformer) model for long-term time series forecasting, addressing the inherent limitations of traditional Transformer-based models in capturing the sequential order. By establishing a causal graph, we identify the confounding relationships, which lead to the erroneous capture of spurious sequential temporal direction information in time series models. The Directional Time Attention, a key component of the model, leverages the front-door adjustment to eliminate the confounder from the causal relationship, ensuring accurate modeling of temporal direction in time series. Additionally, we further analyze the impact of different patching methods and loss functions on prediction performance. The model’s performance is evaluated on nine benchmark datasets, with the results demonstrating its superiority over the State-of-the-Art methods.