39 research outputs found
A New Hybrid Approach For Forecasting Interest Rates
No full textAbstractThe dynamic, non-linear, volatile and complex nature of interest rates makes it hard to predict their future movements. In order to deal with these complexities, the authors propose a two-stage neuro-hybrid forecasting model. In the initial data preprocessing stage, multiple regression analysis is implemented to determine the variables that have the strongest prediction ability. The selected variables are then provided as inputs to a Fuzzy Inference Neural Network to forecast future interest rate values. The proposed hybrid model is implemented using data from the U.S. interest rate market
PRESCRIPTIVE PROCESS ANALYTICS WITH DEEP LEARNING AND EXPLAINABLE ARTIFICIAL INTELLIGENCE
No full textThe proliferation of enterprise information systems allows to capture the digital footprints generated over various user interaction phases. Such transaction data describe the details of the user interactions and the underlying processes on a fine granular level. Building capabilities to analyse the transactional process data is a key success differentiation factor that enables to grasp the user behavior more effectively. In this study, we aim to propose a prescriptive process analytics approach by combining the approaches from the machine learning, process mining and explainable artificial intelligence (XAI) research domains. After examining predictability of the business processes by employing an advanced deep learning approach, this study applies for the first time both in the business process prediction and customer journey analytics research domains an XAI technique, Partial Dependence Plots (PDP), to generate causal explanations. The real-life process data delivered by various information systems of a Dutch autonomous administrative authority were used to investigate the appropriateness of the proposed prescriptive analytics approach. The applied deep learning approach achieves a very good performance with an Area Under ROC Curve of 0.933. The generated explanations with PDP give insights to identify a set of alternative courses-of-actions to prevent the undesired outcomes
Type-2 Fuzzy Clustering and a Type-2 Fuzzy Inference Neural Network for the Prediction of Short-term Interest Rates
No full textAbstractThe following paper discusses the use of a hybrid model for the prediction of short-term US interest rates. The model consists of a differential evolution-based fuzzy type-2 clustering with a fuzzy type-2 inference neural network, after input preprocessing with multiple regression analysis. The model was applied to forecast the US 3- Month T-bill rates. Promising model performance was obtained as measured using root mean square error
Interest Rate Prediction: A Neuro-hybrid Approach with Data Preprocessing
No full textThe following research implements a differential evolution-based fuzzy-type clustering method with a fuzzy inference neural network after input preprocessing with regression analysis in order to predict future interest rates, particularly 3-month T-bill rates. The empirical results of the proposed model is compared against nonparametric models, such as locally weighted regression and least squares support vector machines, along with two linear benchmark models, the autoregressive model and the random walk model. The root mean square error is reported for comparison. © 2014 Taylor & Francis
LOCAL POST-HOC EXPLANATIONS FOR PREDICTIVE PROCESS MONITORING IN MANUFACTURING
No full textThis study proposes an innovative explainable predictive quality analytics solution to facilitate the data-driven decision-making for process planning in manufacturing by combining process mining, machine learning, and explainable artificial intelligence (XAI) methods. For this purpose, after integrating the top-floor and shop-floor data obtained from various enterprise information systems, a deep learning model was applied to predict the process outcomes. Since this study aims to operationalize the delivered predictive insights by embedding them into decision-making processes, it is essential to generate the relevant explanations for domain experts. To this end, two complementary local post-hoc explanation approaches, Shapley values and Individual Conditional Expectation (ICE) plots are adopted, which are expected to enhance the decision-making capabilities by enabling experts to examine explanations from different perspectives. After assessing the predictive strength of the applied deep neural network with relevant binary classification evaluation measures, a discussion of the generated explanations is provided
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
No full textInterpretable and explainable machine learning methods for predictive process monitoring: a systematic literature review
No full textThis study presents a systematic literature review on the explainability and interpretability
of machine learning models within the context of predictive process monitoring. Given the
rapid advancement and increasing opacity of artificial intelligence systems, understanding
the "black-box" nature of these technologies has become critical, particularly for models
trained on complex operational and business process data. Using the PRISMA framework,
this review systematically analyzes and synthesizes the literature of the past decade, in cluding recent and forthcoming works from 2025, to provide a timely and comprehen sive overview of the field. We differentiate between intrinsically interpretable models and
more complex systems that require post-hoc explanation techniques, offering a structured
panorama of current methodologies and their real-world applications. Through this rig orous bibliographic analysis, our research provides a detailed synthesis of the state of
explainability in predictive process mining, identifying key trends, persistent challenges
and a clear agenda for future research. Ultimately, our findings aim to equip researchers
and practitioners with a deeper understanding of how to develop and implement more
trustworthy, transparent and effective intelligent systems for predictive process analytics
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
No full textThis paper introduces a comprehensive, multi-stage machine learning
methodology that effectively integrates information systems and artificial
intelligence to enhance decision-making processes within the domain of
operations research. The proposed framework adeptly addresses common
limitations of existing solutions, such as the neglect of data-driven
estimation for vital production parameters, exclusive generation of point
forecasts without considering model uncertainty, and lacking explanations
regarding the sources of such uncertainty. Our approach employs Quantile
Regression Forests for generating interval predictions, alongside both local
and global variants of SHapley Additive Explanations for the examined
predictive process monitoring problem. The practical applicability of the
proposed methodology is substantiated through a real-world production planning
case study, emphasizing the potential of prescriptive analytics in refining
decision-making procedures. This paper accentuates the imperative of addressing
these challenges to fully harness the extensive and rich data resources
accessible for well-informed decision-making
Stock Market Prediction with Multiple Regression, Fuzzy Type-2 Clustering and Neural Networks
No full textAbstractStock market forecasting research offers many challenges and opportunities, with the forecasting of individual stocks or indexes focusing on forecasting either the level (value) of future market prices, or the direction of market price movement. A three-stage stock market prediction system is introduced in this article. In the first phase, Multiple Regression Analysis is applied to define the economic and financial variables which have a strong relationship with the output. In the second phase, Differential Evolution-based type-2 Fuzzy Clustering is implemented to create a prediction model. For the third phase, a Fuzzy type-2 Neural Network is used to perform the reasoning for future stock price prediction. The results of the network simulation show that the suggested model outperforms traditional models for forecasting stock market prices
A Novel Business Process Prediction Model Using a DeepLearning Method
No full textThe ability to proactively monitor business pro-cesses is a main competitive differentiator for firms. Processexecution logs generated by process aware informationsystems help to make process specific predictions forenabling a proactive situational awareness. The goal of theproposed approach is to predict the next process event fromthe completed activities of the running process instance,based on the execution log data from previously completedprocess instances. By predicting process events, companiescan initiate timely interventions to address undesired devi-ations from the desired workflow. The paper proposes amulti-stage deep learning approach that formulates the nextevent prediction problem as a classification problem. Fol-lowing a feature pre-processing stage with n-grams andfeature hashing, a deep learning model consisting of anunsupervised pre-training component with stacked autoen-coders and a supervised fine-tuning component is applied.Experiments on a variety of business process log datasetsshow that the multi-stage deep learning approach providespromising results. The study also compared the results toexisting deep recurrent neural networks and conventionalclassification approaches. Furthermore, the paper addressesthe identification of suitable hyperparameters for the pro-posed approach, and the handling of the imbalanced nature ofbusiness process event datasets
