简化神经网络架构可大幅降低计算开销，同时保持预测精度。
On the Simplification of Neural Network Architectures for Predictive Process Monitoring
- 通过削减参数量和深度，测试模型简化对预测性能的影响。
- Transformer缩小85%后性能仅下降2-3%，LSTM在等待时间预测上更敏感。
- 适合追求高效部署的工业流程监控应用开发者参考。
预测性流程监控（PPM）旨在利用历史事件数据预测正在进行的流程实例的未来行为，支持主动决策。尽管近期进展依赖于LSTM和Transformer等深度学习模型，但其高计算成本阻碍了实际应用。已有研究探索了数据缩减技术和替代特征编码方式，但对模型架构本身的简化影响仍缺乏深入探讨。本文分析了减少模型复杂度（包括参数量和结构深度）对预测性能的影响，采用两种成熟的PPM方法，在五个不同事件日志上验证。结果显示，将Transformer模型规模缩小85%后，各类PPM任务的性能仅下降2-3%；而LSTM在等待时间预测方面表现稍差。总体表明，大幅简化模型结构可在不显著牺牲准确性的情况下实现更高效率与可扩展性。
原文摘要 · Abstract (English)
Predictive Process Monitoring (PPM) aims to forecast the future behavior of ongoing process instances using historical event data, enabling proactive decision-making. While recent advances rely heavily on deep learning models such as LSTMs and Transformers, their high computational cost hinders practical adoption. Prior work has explored data reduction techniques and alternative feature encodings, but the effect of simplifying model architectures themselves remains underexplored. In this paper, we analyze how reducing model complexity, both in terms of parameter count and architectural depth, impacts predictive performance, using two established PPM approaches. Across five diverse event logs, we show that shrinking the Transformer model by 85% results in only a 2-3% drop in performance across various PPM tasks, while the LSTM proves slightly more sensitive, particularly for waiting time prediction. Overall, our findings suggest that substantial model simplification can preserve predictive accuracy, paving the way for more efficient and scalable PPM solutions.
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