A new approach based on association rules to add explainability to time series forecasting models

Troncoso-García, A. R.; Martínez-Ballesteros, M.; Martínez‐Álvarez, Francisco; Troncoso, Alicia

doi:10.1016/j.inffus.2023.01.021

Cited by 18 publications

(2 citation statements)

References 48 publications

(60 reference statements)

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“…It is difcult to observe the changes of data neuron by neuron, and research on explainable machine learning has generated an enthusiastic response in both academia and industry. A novel method to explain black-box models [38] is proposed, which employs numeric association rules to explain and interpret multistep time series forecasting models. A new deep learning architecture xDNN [39] is proposed, which combines reasoning and learning in a synergy and explains its efciency in terms of time and computational resources.…”

Section: Ex Post Interpretabilitymentioning

confidence: 99%

A Factor Marginal Effect Analysis Approach and Its Application in E-Commerce Search System

Wang,

Sachurengui,

Zhang

et al. 2023

International Journal of Intelligent Systems

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Feature explanation plays an increasingly essential role in the e-commerce search platform. Most of the existing studies focus on modeling the user’s interests to estimate the click-through rate (CTR). A good e-commerce system not only needs precise ranking to inspire users’ shopping desire but also needs feature explanation to meet the demands of shop owners. The e-commerce traffic health of merchants is very important. How to effectively achieve shop owners’ multiple goals still remains as an open problem. In industrial search systems, merchants’ key demands mainly include two aspects. On the one hand, merchants want to know rule analysis of online traffic distribution, so as to help them understand the logistics of online traffic. On the other hand, they need relevant online traffic participation tools, which instruct them to participate. To address these issues, we propose a factor marginal effect analysis approach (FMEA) based on game theory, which can compute the contribution of one-dimensional features to the enhancement of online traffic. First, we use machine learning to model the business target. Then, we improve the SHAP value algorithm, which can provide clear business insights. Finally, we calculate the marginal effect of each feature on the business outcome. In this way, we provide a traffic analysis guidance method and address merchants’ participation challenges. In fact, the FMEA has been deployed in a real-world Large-Internet-Company’s App search systems and successfully serves online e-commerce service to over hundreds of millions of consumers. Our approach can guide operational decisions effectively and bring +10.05% revenue for the flow index, +7.54% for the user feedback index, and +2.46% for the service index.

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Section: Ex Post Interpretabilitymentioning

confidence: 99%

A Factor Marginal Effect Analysis Approach and Its Application in E-Commerce Search System

Wang,

Sachurengui,

Zhang

et al. 2023

International Journal of Intelligent Systems

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“…For this reason, Data Mining (DM) approaches have been used over the years to create scheduling algorithms that are more flexible because they are able to consider different aspects when choosing the sequence of activities. In particular, Association Rules (ARs) are a powerful tool to support decision-making processes, because they are able to find hidden relationships between different parameters in large datasets (Troncoso-García et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment

DEL GALLO,

CIARAPICA,

MAZZUTO

et al. 2023

Management and Production Engineering Review

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Production problems have a significant impact on the on-time delivery of orders, resulting in deviations from planned scenarios. Therefore, it is crucial to predict interruptions during scheduling and to find optimal production sequencing solutions. This paper introduces a selflearning framework that integrates association rules and optimisation techniques to develop a scheduling algorithm capable of learning from past production experiences and anticipating future problems. Association rules identify factors that hinder the production process, while optimisation techniques use mathematical models to optimise the sequence of tasks and minimise execution time. In addition, association rules establish correlations between production parameters and success rates, allowing corrective factors for production quantity to be calculated based on confidence values and success rates. The proposed solution demonstrates robustness and flexibility, providing efficient solutions for Flow-Shop and Job-Shop scheduling problems with reduced calculation times. The article includes two Flow-Shop and Job-Shop examples where the framework is applied.

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Olive Oil Fly Population Pest Forecasting Using Explainable Deep Learning

Chacón-Maldonado,

Troncoso-García,

Martínez-Álvarez

et al. 2023

18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023)

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A new approach based on association rules to add explainability to time series forecasting models

Cited by 18 publications

References 48 publications

A Factor Marginal Effect Analysis Approach and Its Application in E-Commerce Search System

A Factor Marginal Effect Analysis Approach and Its Application in E-Commerce Search System

A Combination of Association Rules and Optimization Model to Solve Scheduling Problems in an Unstable Production Environment

Olive Oil Fly Population Pest Forecasting Using Explainable Deep Learning

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