<![CDATA[Business process prediction is essential for monitoring workflows and ensuring service quality. A key task in this area, remaining time prediction, focuses on estimating process duration and has been extensively studied. While Long Short-Term Memory (LSTM) networks are widely adopted, their limited parallelization and sequential modeling capabilities constrain performance. To address these limitations, we propose a remaining time prediction approach based on a bidirectional Quasi-Recurrent Neural Network (QRNN) with an attention mechanism. Specifically, the bidirectional QRNN is employed to construct the prediction model, while the attention mechanism enhances its ability to extract feature information. Next, a transfer training iteration strategy based on different trace prefix lengths is designed to address the imbalance in trace lengths. Then, a Word2Vec-based event representation learning approach is introduced to generate similarity vector of adjacent events, further improving prediction accuracy. Finally, using five publicly real-life event logs, the proposed approach is evaluated against state-of-the-art approaches. Experimental results demonstrate that it improves average prediction accuracy by nearly 15% while reducing average model training time by approximately 26%.]]>
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