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.

Cash flow prediction is important. It can help increase returns and improve the allocation of capital in healthy, mature firms as well as prevent fast-growing firms, or firms in distress, from running out of cash. In this paper, we predict accounts receivable cash flows employing methods applicable to companies with many customers and many transactions such as e-commerce companies, retailers, airlines and public transportation firms with sales in multiple regions and countries. We first discuss "classic" forecasting techniques such as ARIMA and Facebook's TM Prophet before moving on to neural networks with multi-layered perceptrons (MLP) and, finally, Long Short-Term Memory (LSTM) networks, that are particularly useful for time series forecasting but were until now not used for cash flows. Our evaluation demonstrates this range of methods to be of increasing sophistication, flexibility and accuracy. We also introduce a new performance measure, interest opportunity cost (IOC), that incorporates interest rates and the cost of capital to optimize the models in a financially meaningful, money-saving, way. Keywords cash flow prediction • accounts receivable • neural networks • LSTM • MLP • ARIMA • Prophet

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Advances in AI, and especially machine learning, are increasingly drawing research interest and efforts towards predictive process monitoring, the subfield of process mining (PM) that concerns predicting next events, process outcomes and remaining execution times. Unfortunately, researchers use a variety of datasets and ways to split them into training and test sets. The documentation of these preprocessing steps is not always complete. Consequently, research results are hard or even impossible to reproduce and to compare between papers. At times, the use of non-public domain knowledge further hampers the fair competition of ideas. Often the training and test sets are not completely separated, a data leakage problem particular to predictive process monitoring. Moreover, test sets usually suffer from bias in terms of both the mix of case durations and the number of running cases. These obstacles pose a challenge to the field's progress. The contribution of this paper is to identify and demonstrate the importance of these obstacles and to propose preprocessing steps to arrive at unbiased benchmark datasets in a principled way, thus creating representative test sets without data leakage with the aim of levelling the playing field, promoting open science and contributing to more rapid progress in predictive process monitoring.

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