# Process Model Forecasting Using Time Series Analysis of Event Sequence Data

arXiv: https://arxiv.org/abs/2105.01092 · HTML: https://mlsanity.com/paper/2105.01092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 9 of 9)

- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Outcome-Oriented Predictive Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/1707.06766/) (2017)
- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Comprehensive Process Drift Detection with Visual Analytics](https://mlsanity.com/paper/1907.06386/) (2019)
- [Detecting sudden and gradual drifts in business processes from execution traces](https://mlsanity.com/paper/2005.04016/) (2020)
- [An Entropic Relevance Measure for Stochastic Conformance Checking in Process Mining](https://mlsanity.com/paper/2007.09310/) (2020)

## Built on by (most cited first, 1 of 1)

- [PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networks](https://mlsanity.com/paper/2312.09741/) (2023)

## Abstract (arXiv)

Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next activity, remaining time, and outcome prediction. At the model level, there is a notable void. It is the ambition of this paper to fill this gap. To this end, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable future state of the overall process. Such a forecast helps to investigate the consequences of drift and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding forecasting techniques can be applied. Our implementation demonstrates the accuracy of our technique on real-world event log data.