The paper adds explanation to predictive process monitoring.
problem Equipping predictive business process monitoring with explanation capabilities.
method Used game theory of Shapley Values to obtain robust explanations.
result First time explanations given in predictive business process monitoring.
Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel m…
The paper improves neural network predictions by integrating process knowledge.
problem Improving neural network predictions for process execution data.
method Integrates background process knowledge into neural networks with attention mechanisms.
result Improves prediction accuracy for process execution data.
Study uses deep learning to predict asset prices, finds complex target processes lead to meaningless predictions.
problem Complexity of successful price prediction models hinders understanding.
method Deep learning models for high-frequency price prediction, focusing on volatility and directional prediction.
result Inadequately defined target price process renders predictions meaningless.
GNP models predictive correlations and outperforms NPs.
problem Training and understanding of Neural Processes.
method Proposed a new model, Gaussian Neural Process (GNP), which incorporates translation equivariance and provides universal approximation guarantees.
result Demonstrates encouraging performance and provides universal approximation guarantees.
FlowMO uses Gaussian Processes for molecular property prediction with uncertainty.
problem Predicting molecular properties with uncertainty for small datasets.
method Gaussian Processes implemented in FlowMO, built on GPflow and RDKit.
result Comparable predictive performance to deep learning but superior uncertainty calibration.
Proposes a model for predicting events from event streams.
problem Predicting events like part replacement and failure in manufacturing and teleservice systems.
method Non-parametric prognostic framework using MGCP modulated Poisson processes.
result MGCP prior facilitates sharing of information and analysis of flexible event patterns.
Study on predictable forward processes in trading without frequent evaluations.
problem Trading performance evaluation times not matching trading times.
method Solving a linear functional equation to construct predictable forward processes.
result Predictable forward processes are inherently myopic and optimal strategies do not use future information.
DSVNP uses global and local latent variables for improved neural process predictions.
problem Limited expressiveness of vanilla neural processes in capturing target-specific local variation.
method Introduces DSVNP combining global and local latent variables for prediction.
result Competitive prediction performance in multi-output regression and uncertainty estimation.
Study evaluates post-processing methods for improving solar power forecasts.
problem Improving accuracy of probabilistic solar energy forecasts through model chain approaches.
method Systematically evaluates different post-processing strategies for ensemble weather forecasts and direct solar power forecasting.
result Post-processing significantly improves solar power generation forecasts, especially when applied to power predictions.
Paper introduces a hybrid GPR model for more interpretable RUL prediction in aeroengine.
problem Challenges in interpreting and modeling uncertainty in RUL prediction models.
method Modified Gaussian Process Regression (GPR) with temporal feature extraction.
result Effective prediction of RUL intervals with transparent feature significance.
Modern predictive analytics underpinned by machine learning techniques has become a key enabler to the automation of data-driven decision making. In the context of business process management, predictive analytics has been applied to making predictions about the future state of an ongoing business process instance, for…
CQNPs enhance predictive performance and distribution modeling using quantile regression.
problem Limited predictive likelihood of Gaussian models for complex distributions.
method Introducing Conditional Quantile Neural Processes (CQNPs) that focus on estimating informative quantiles.
result Significant improvements in predictive performance and better modeling of multimodal distributions.
New methods for inferring, predicting, and estimating continuous-time, discrete-event processes.
problem Inferring, predicting, and estimating entropy rate of continuous-time, discrete-event processes.
method Bayesian structural inference extended with neural networks.
result Methods are competitive for prediction and entropy-rate estimation with state-of-the-art.
Study proposes explainable analytics for manufacturing process planning.
problem Improving data-driven decision-making in manufacturing.
method Combines process mining, machine learning, and XAI. Uses deep learning for prediction and Shapley values/ICE plots for explanations.
result Enhanced decision-making capabilities through local post-hoc explanations.
We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the covariance kernel of a G…
This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical predictive variance, which is a lower bound on the mean square-error (MSE). This approach, however, does not take into account that the stat…
This research tackles group fairness in predictive process monitoring by ensuring predictions are independent of sensitive group membership.
problem Predictive models using biased historical data can perpetuate unfair behavior in new cases.
method Investigates independence through metrics like ΔDP and a composite loss function balancing predictive performance and fairness.
result Proposes and validates a composite loss function for training models that balance fairness and performance.
Dividing local Gaussian processes improve real-time prediction efficiency.
problem Efficient online prediction for large data sets.
method Iterative data-driven division of input space for sublinear computational complexity.
result Sublinear computational complexity in real-time prediction.
Paper develops a method to predict spatial point processes with guarantees.
problem Predicting the number of events in space with uncertainty.
method Regularized method to learn spatial models with out-of-sample guarantees.
result Method provides valid prediction intervals even when model is misspecified.
Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring…
We address the issue of knots selection for Gaussian predictive process methodology. Predictive process approximation provides an effective solution to the cubic order computational complexity of Gaussian process models. This approximation crucially depends on a set of points, called knots, at which the original proces…
Meta-learning improves event prediction from short sequences.
problem Predicting events from short sequences is challenging.
method Meta-learning approach using recurrent neural networks and monotonic neural networks.
result Meta-learning enhances long-term prediction performance.
Predictive process monitoring is concerned with the analysis of events produced during the execution of a business process in order to predict as early as possible the final outcome of an ongoing case. Traditionally, predictive process monitoring methods are optimized with respect to accuracy. However, in environments …
Improved Gaussian process experts model for complex data.
problem Limitations of standard Gaussian processes: scalability and predictive performance.
method Proposes a new mixture model of Gaussian process experts based on kernel stick-breaking processes.
result Improved predictive performance compared to existing models.
New neural process models produce correlated predictions for better estimation tasks.
problem Need for models that can handle correlated predictions for tasks like weather forecasting.
method Developed new Neural Process models that can produce correlated predictions and support exact maximum likelihood training.
result Improved predictive performance on various experiments with synthetic and real data.
Enhances fairness in predictions without sacrificing accuracy.
problem Balancing fairness and predictive performance in machine learning.
method Model ensemble-based post-processing framework.
result Framework effectively enhances fairness while maintaining predictive accuracy.
Paper proposes a GAN-based method for better next event prediction in business processes.
problem Insufficient training data and sub-optimal network configuration limit deep learning approaches to next event prediction.
method Adversarial training framework using Generative Adversarial Networks (GANs) for sequential temporal data.
result The proposed approach achieves at least as good accuracy as non-adversarial methods and outperforms them in accuracy and prediction earliness.
The paper integrates multiple Gaussian process predictions using Monte Carlo sampling.
problem Accurate prediction of variables using multiple models.
method Log-linear pooling of Gaussian process predictions, combined with Monte Carlo sampling.
result The log-linear pooling method improves prediction accuracy compared to linear pooling.
MAGMA uses a common mean process to improve multi-step-ahead time series forecasting.
problem Improving multiple-step-ahead predictions for time series data.
method Proposes a novel multi-task Gaussian process framework with a common mean process for sharing information across tasks.
result Significantly improves predictive performances, even far from observations, and reduces computational complexity.
GNet uses Gaussian processes for scalable, flexible neural networks.
problem Large-scale predictive modeling with high computational and storage costs.
method GNet employs Gaussian processes with nonparametric activation functions and a fast algorithm for training and predictions.
result GNet achieves competitive performance across various test problems, including nonlinear function prediction and real-world data regression.
GNet uses Gaussian processes for scalable, flexible neural networks.
problem Large-scale predictive modeling with high computational and storage costs.
method GNet employs Gaussian processes with nonparametric activation functions and a fast algorithm for efficient training and predictions.
result GNet achieves competitive performance across various test problems, including nonlinear function prediction and real-world data regression.
Mixture of multi-task GPs for clustering and prediction of functional data.
problem Handling multi-task learning, clustering, and prediction for functional data.
method A mixture of multi-task Gaussian processes with a variational EM algorithm for hyper-parameter optimization.
result Enhanced predictive performance for group-structured data.
Two methods improve Gaussian process predictive distributions' calibration.
problem Improving the reliability of Gaussian process predictive intervals.
method Introduces two methods: cps-gp and bcr-gp, both adapting conformal predictive systems to GP interpolation.
result Both methods provide finite-sample marginal calibration and smooth predictive distributions.
SIP corrects model bias in Bayesian ML.
problem Model selection biases predictions in Bayesian ML.
method Sparse Implicit Processes (SIP) for flexible, trainable predictions.
result SIP provides better predictive distributions than initial models.
Proposes GPLFR for predicting high-dimensional outputs with few data.
problem Predicting high-dimensional outputs from limited data.
method GPLFR combines Gaussian process and linear-Gaussian decoding for high-dimensional prediction.
result GPLFR outperforms existing methods in predicting high-dimensional outputs.
The paper uses conformal prediction for forecasting conflict sequences in Markov processes.
problem Forecasting future conflict states for national policy decisions.
method Conformal prediction on Markov processes for temporally-dependent data.
result Conformal prediction provides valid uncertainty quantification and robustness to model misspecification.
Improved Gaussian Process model for predicting trajectories without independence assumption errors.
problem Incorrect independence assumption in previous work on Gaussian Process uncertainty propagation.
method Proposed a novel piecewise linear approximation to correct the independence assumption in continuous models.
result Corrected the independence assumption in Gaussian Process models for predicting trajectories.
The article improves GP interpolation calibration using conformal prediction.
problem GP interpolation often produces poorly calibrated prediction intervals.
method Integrates conformal prediction with Gaussian process models.
result CP methods enhance prediction interval calibration without sacrificing accuracy.
In complex processes, various events can happen in different sequences. The prediction of the next event given an a-priori process state is of importance in such processes. Recent methods have proposed deep learning techniques such as recurrent neural networks, developed on raw event logs, to predict the next event fro…
In predictive process analytics, current and historical process data in event logs is used to predict the future, e.g., to predict the next activity or how long a process will still require to complete. Recurrent neural networks (RNN) and its subclasses have been demonstrated to be well suited for creating prediction m…
Deep learning predicts path-dependent processes from historical data.
problem Predicting path-dependent processes using historical data.
method Nonparametric regression with deep neural networks.
result Deep learning method converges to theoretical predictions as observation frequency increases.
A new method for faster prediction in distributed Gaussian processes.
problem Inefficient aggregation of distributed Gaussian processes with correlations.
method Proposes a novel approach for aggregated prediction in distributed GPs that incorporates correlations among experts.
result Results in more stable predictions in less time compared to state-of-the-art methods.
Paper uses neural networks to predict NOx emissions from gas turbines.
problem Predicting NOx emissions from degrading gas turbines.
method Applied neural network algorithm to model NOx emissions from nine process variables.
result Neural network model optimizes process variables for minimal NOx emissions.
ConvNP improves SP prediction with translation equivariance and coherent samples.
problem Predicting stationary stochastic processes with coherent samples.
method Convolutional Neural Processes (ConvNP) with a new maximum-likelihood objective.
result ConvNP outperforms standard NPs and demonstrates strong generalization on various tasks.
We use GANs and signatures to approximate conditional laws in filtering and prediction of diffusion processes.
problem Approximating conditional laws for diffusion processes with noisy observations.
method Conditional GANs combined with signatures for approximation.
result Efficient approximation of conditional laws for diffusion processes.
Paper quantifies uncertainty in probabilistic models using Gaussian Processes.
problem Assessing reliability of probabilistic machine learning predictions.
method Systematic framework for estimating epistemic and aleatoric uncertainty, using Gaussian Processes and Monte Carlo sampling.
result Effective approach for quantifying prediction confidence in probabilistic models.
New MTPP model offers interpretable predictions with state-of-the-art performance.
problem Inexpressive models lack interpretability, while neural models sacrifice interpretability for performance.
method Extends Hawkes process to a hypernetwork with a latent space, making it flexible and interpretable.
result Achieves state-of-the-art performance across various tasks and metrics.