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

by   Farbod Taymouri, et al.

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.


page 1

page 2

page 3

page 4


Encoder-Decoder Generative Adversarial Nets for Suffix Generation and Remaining Time Predication of Business Process Models

This paper proposes an encoder-decoder architecture grounded on Generati...

ProcessTransformer: Predictive Business Process Monitoring with Transformer Network

Predictive business process monitoring focuses on predicting future char...

Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction

Predictive process monitoring aims to predict future characteristics of ...

Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling

We target modeling latent dynamics in high-dimension marked event sequen...

Conditional Generation of Temporally-ordered Event Sequences

Models encapsulating narrative schema knowledge have proven to be useful...

Large-scale End-of-Life Prediction of Hard Disks in Distributed Datacenters

On a daily basis, data centers process huge volumes of data backed by th...

Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors

Reliable remaining time prediction of ongoing business processes is a hi...

Please sign up or login with your details

Forgot password? Click here to reset