<p>Time series forecasting has broad applications in fields such as finance, energy management, and weather prediction. PatchTST, a widely recognized model, has significantly improved the input length and accuracy of time series predictions. However, it uniformly treats all patches without considering their varying impacts on future predictions and relies heavily on global attention, often neglecting local information. To address these issues, we have developed an enhanced model: Dynamic Feature Weighting PatchTST (DynamicPatchTST). This model features three key enhancements: (i) a dynamic feature weighting strategy that assigns weights to each patch based on the characteristics of individual time series, emphasizing more impactful patches; (ii) a linear embedding method to replace positional encoding, preventing the over-adjustment or redundant modification of patch weights; (iii) a dual-pathway strategy that integrates local and global information, with the local pathway refined through gated adaptive adjustments to enhance the model’s ability to capture local details. Extensive experiments across multiple standard time series datasets (e.g., ETT, Exchanges, Weather) have demonstrated that our DynamicPatchTST significantly outperforms existing state-of-the-art models, with an average improvement of 4.4% in Mean Squared Error (MSE). This work provides a novel perspective on time series forecasting and paves the way for future advancements in the field.</p>