Remaining time prediction helps enterprises improve the quality and efficiency of business process execution. Although existing deep learning methods have shown improvement in remaining time prediction, they still face challenges when dealing with complex business processes. These challenges include insufficient utilization of time features and limited ability to extract local features, leaving room for improvement in prediction accuracy. This study proposes a remaining time prediction method based on the improved Transformer encoder model. Existing methods ignore event time features and struggle to capture local dependencies. To address these limitations, this study introduces a time feature encoding module and a local dependency enhancement module into the model. The time encoding module constructs a semantically rich and discriminative event time representation by embedding learning and multi-granularity concatenation. The local dependency enhancement module uses convolutional neural networks to extract fine-grained local features from the trajectory prefix after processing with the Transformer encoder. Experiments show that integrating time features and local dependency enhancement improves the prediction accuracy of the remaining time for complex business processes.