Abstract: Business process remaining time prediction is a core task in predictive process monitoring. Its accuracy provides critical decision support for process optimization, resource scheduling, anomaly early warning, and Quality of Service (QoS) guarantee. However, existing methods generally suffer from two major limitations. First, deep learning models based on Long Short-Term Memory (LSTM) or Transformer, while capable of learning complex temporal dependencies, often rely on recurrent structures or self-attention mechanisms. These models involve large parameter scales and high computational complexity, leading to significant inference latency for long sequences and hindering real-time deployment in computational resource-constrained environments. Second, the utilization of temporal information remains coarse-grained in current research. Most methods rely on single temporal attributes such as timestamps or event intervals, ignoring the importance variations of multi-dimensional temporal semantics—including duration, cumulative execution time, and waiting time—across different process stages. This failure to fully exploit key driving factors limits overall prediction performance. To address these issues, this paper proposes a lightweight business process remaining time prediction method based on adaptive time-weighting, termed LTW-Mixer. First, a multi-dimensional temporal feature system is systematically constructed, covering duration, cumulative execution time, waiting time, resource response time, and periodic information (e.g., day of the week and hour of the day). Building on this, an adaptive time-weighting mechanism is designed to dynamically adjust the contribution of each temporal feature based on the process context. This mechanism enhances key temporal information while suppressing redundant features, thereby improving the model's ability to capture critical time-driven factors. Finally, to balance prediction accuracy and model efficiency, a lightweight backbone based on MLP-Mixer is developed. Sequence mixing and channel mixing are employed to replace the computationally intensive self-attention mechanism, achieving efficient interaction across both temporal and feature dimensions. Additionally, a gated fusion mechanism is introduced to dynamically integrate activity and temporal semantics, allowing the model to adaptively regulate the contribution ratios of heterogeneous features and effectively capture long-range dependencies while maintaining low computational overhead. Experimental results on six public real-world business process datasets demonstrate that LTW-Mixer achieves the lowest Mean Absolute Error (MAE) across all datasets. Specifically, for the BPIC2015_2 and BPIC2015_5 datasets, the MAE is reduced by approximately 26.7% and 39.5%, respectively, compared to the state-of-the-art baseline models. Ablation studies confirm that the multi-dimensional temporal features, adaptive weighting mechanism, gated fusion, and MLP-Mixer backbone all contribute significantly to the model's performance. Furthermore, feature weight analysis and sensitivity experiments show that the model can dynamically prioritize key factors such as waiting time and resource response time across different business scenarios, demonstrating strong interpretability. In terms of lightweight performance, LTW-Mixer requires only 0.19 M parameters and 0.71 MB of storage, with a peak GPU memory usage of 57.15 MB and a per-sample inference time of only 0.023 ms. These results outperform all comparison models, proving that LTW-Mixer significantly reduces storage and computational costs while ensuring high accuracy, thus exhibiting excellent real-time responsiveness and potential for industrial deployment.

摘要： 业务流程剩余时间预测是预测性流程监控中的关键任务，其预测结果能够为业务流程优化、资源调度、异常预警及服务质量保障提供重要决策支持。然而，现有业务流程剩余时间预测方法普遍存在两方面不足：一是基于长短期记忆网络（LSTM）和Transformer等深度学习模型虽然能够学习复杂的流程时序依赖关系，但通常依赖循环结构或自注意力机制，模型参数规模较大、计算复杂度较高，在长序列预测场景下推理延迟明显，难以满足计算资源受限环境下的实时部署需求；二是已有研究对时间信息的利用仍较为粗粒度，大多仅采用时间戳、事件间隔等单一时间属性作为输入，忽略了持续时间、累计执行时间以及等待时间等多维时间语义在不同流程阶段的重要性差异，导致模型难以充分挖掘影响流程剩余时间的关键驱动因素，从而限制了预测性能。针对上述问题，提出一种基于自适应时间加权的轻量级业务流程剩余时间预测方法LTW-Mixer。首先，围绕业务流程执行过程中不同层次的时间语义，构建由持续时间、累计执行时间、等待时间、资源响应时间及星期、小时等周期性时间信息组成的多维时间特征体系。在此基础上，为解决不同时间特征在不同流程阶段贡献程度不同的问题，设计时间特征自适应加权机制，根据流程上下文动态调整各时间特征的贡献程度，强化关键时间信息并抑制冗余特征，提高模型对关键时间驱动因素的建模能力。最后，为兼顾预测精度与模型轻量化，构建基于MLP-Mixer的轻量级骨干网络，以序列混合和通道混合替代传统Transformer中的自注意力计算，实现时间维度与特征维度的高效交互；同时设计门控融合机制，对活动语义信息与时间语义信息进行动态融合，使模型能够根据流程上下文自适应调节两类信息的贡献比例，从而增强异构特征协同建模能力，在保持较低计算复杂度的同时有效捕获长距离时序依赖关系。本文在6个公开的真实业务流程数据集上进行了对比实验，结果表明，LTW-Mixer在所有数据集上均取得最低的平均绝对误差（MAE），例如在BPIC2015_2和BPIC2015_5数据集中，本方法的MAE较现有性能最优对比模型分别降低约26.7%和39.5%；消融实验进一步验证了多维时间特征、自适应时间加权机制、门控融合机制及MLP-Mixer骨干网络均对模型性能具有显著贡献；时间特征权重分析和敏感性实验表明，模型能够根据不同业务场景动态关注等待时间、资源响应时间及周期性时间等关键时间因素，具有较好的可解释性。在轻量化性能方面，LTW-Mixer模型参数量仅为0.19M，模型存储大小仅为0.71MB，GPU显存占用57.15MB，单样本平均推理时间仅为0.023ms，均优于各对比模型，在保证预测精度的同时显著降低了模型存储开销和计算成本，具有良好的实时响应能力和工程部署潜力。