The early outcome prediction of ongoing or completed processes confers competitive advantage to organizations. The performance of classic machine learning and, more recently, deep learning techniques such as Long Short-Term Memory (LSTM) on this type of classification problem has been thorougly investigated. Recently, much research focused on applying Convolutional Neural Networks (CNN) to time series problems including classification, however not yet to outcome prediction. The purpose of this paper is to close this gap and compare CNNs to LSTMs. Attention is another technique that, in combination with LSTMs, has found application in time series classification and was included in our research. Our findings show that all these neural networks achieve satisfactory to high predictive power provided sufficiently large datasets. CNNs perfom on par with LSTMs; the Attention mechanism adds no value to the latter. Since CNNs run one order of magnitude faster than both types of LSTM, their use is preferable. All models are robust with respect to their hyperparameters and achieve their maximal predictive power early on in the cases, usually after only a few events, making them highly suitable for runtime predictions. We argue that CNNs' speed, early predictive power and robustness should pave the way for their application in process outcome prediction.
翻译:对进行中或完成过程的早期结果预测给各组织带来了竞争优势。传统机器学习的绩效,以及最近这类分类问题的长期短期内存(LSTM)等深层次学习技术的绩效,已经进行了深入调查。最近,许多研究侧重于将革命神经网络(CNN)应用于时间序列问题,包括分类,尽管还没有结果预测。本文的目的是缩小这一差距,将CNN与LSTMs比较。注意是另一种技术,它与LSTMS相结合,在时间序列分类中应用,并被纳入我们的研究中。我们的研究结果表明,所有这些神经网络在高预测能力方面都取得了令人满意的结果,提供了足够大的数据集。CNNs与LSTMs保持同步;注意机制没有为后者增加任何价值。由于CNN比LSTM两种类型运行一个数量级的速度都快,因此使用它们更为可取。所有模型在超参数方面都很稳健,在案件中都实现了最强的预测能力,通常在少数事件之后就被包括在我们的研究中。我们发现,所有这些神经网络都能够满足高超时速预测结果。我们认为CNMS的早期预测。