# A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences

arXiv: https://arxiv.org/abs/2102.07298 · HTML: https://mlsanity.com/paper/2102.07298/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 12 of 12)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [A* Sampling](https://mlsanity.com/paper/1411.0030/) (2014)
- [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https://mlsanity.com/paper/1611.09904/) (2016)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Tensor2Tensor for Neural Machine Translation](https://mlsanity.com/paper/1803.07416/) (2018)
- [Amortised MAP Inference for Image Super-resolution](https://mlsanity.com/paper/1610.04490/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction](https://mlsanity.com/paper/2003.11268/) (2020)

## Built on by (most cited first, 17 of 17)

- [Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction](https://mlsanity.com/paper/2105.07111/) (2021)
- [What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learning](https://mlsanity.com/paper/2110.10225/) (2021)
- [Modality-aware Transformer for Financial Time series Forecasting](https://mlsanity.com/paper/2310.01232/) (2023)
- [Neuro-Symbolic Predictive Process Monitoring](https://mlsanity.com/paper/2509.00834/) (2025)
- [Trace Encoding in Process Mining: a survey and benchmarking](https://mlsanity.com/paper/2301.02167/) (2023)
- [Modeling Events and Interactions through Temporal Processes -- A Survey](https://mlsanity.com/paper/2303.06067/) (2023)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction](https://mlsanity.com/paper/2312.08847/) (2023)
- [SynHAT: A Two-stage Coarse-to-Fine Diffusion Framework for Synthesizing Human Activity Traces](https://mlsanity.com/paper/2604.14705/) (2026)
- [Goal-Oriented Next Best Activity Recommendation using Reinforcement Learning](https://mlsanity.com/paper/2205.03219/) (2022)
- [Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction](https://mlsanity.com/paper/2604.00653/) (2026)
- [Clustering-based Aggregations for Prediction in Event Streams](https://mlsanity.com/paper/2210.09738/) (2022)
- [Encoder-Decoder Model for Suffix Prediction in Predictive Monitoring](https://mlsanity.com/paper/2211.16106/) (2022)
- [Data is Moody: Discovering Data Modification Rules from Process Event Logs](https://mlsanity.com/paper/2312.14571/) (2023)
- [An Uncertainty-Aware ED-LSTM for Probabilistic Suffix Prediction](https://mlsanity.com/paper/2505.21339/) (2025)
- [Decision-Aware Suffix Prediction and Reasoning of Business Processes](https://mlsanity.com/paper/2609.06169/) (2026)
- [Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes](https://mlsanity.com/paper/2609.21382/) (2026)

## Abstract (arXiv)

Event suffix and remaining time prediction are sequence to sequence learning tasks. They have wide applications in different areas such as economics, digital health, business process management and IT infrastructure monitoring. Timestamped event sequences contain ordered events which carry at least two attributes: the event's label and its timestamp. Suffix and remaining time prediction are about obtaining the most likely continuation of event labels and the remaining time until the sequence finishes, respectively. Recent deep learning-based works for such predictions are prone to potentially large prediction errors because of closed-loop training (i.e., the next event is conditioned on the ground truth of previous events) and open-loop inference (i.e., the next event is conditioned on previously predicted events). In this work, we propose an encoder-decoder architecture for open-loop training to advance the suffix and remaining time prediction of event sequences. To capture the joint temporal dynamics of events, we harness the power of adversarial learning techniques to boost prediction performance. We consider four real-life datasets and three baselines in our experiments. The results show improvements up to four times compared to the state of the art in suffix and remaining time prediction of event sequences, specifically in the realm of business process executions. We also show that the obtained improvements of adversarial training are superior compared to standard training under the same experimental setup.