Explainable AI (XAI) for Cloud Resource Forecasting in E-Commerce Environments Article Swipe
S. Singh
,
V. Ravindran
,
S. H. Patil
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.36948/ijfmr.2025.v07i04.51796
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.36948/ijfmr.2025.v07i04.51796
The rapid growth of e-commerce has intensified the demand for accurate and trustworthy cloud resource forecasting to ensure seamless service delivery during volatile and unpredictable workload fluctuations. Traditional black-box machine learning models, while powerful in prediction, often fail to provide the transparency necessary for stakeholders to trust and effectively manage automated resource allocation decisions. This paper proposes a novel hybrid framework that integrates Explainable Artificial Intelligence (XAI) techniques into cloud resource forecasting for e-commerce environments, combining the predictive strength of advanced sequential models like LSTM with interpretable surrogate models and post-hoc explanation methods such as SHAP and LIME. Using real-world workload data from a leading e-commerce platform, the study demonstrates that the hybrid model achieves superior predictive performance—reflected by lower RMSE, MAE, and MAPE values—while delivering clear, actionable explanations that align with stakeholders’ operational knowledge. Experimental results under realistic scenarios, including high-demand events like flash sales, confirm that embedding explainability into forecasting pipelines enhances operational trust, supports proactive resource provisioning, and aligns with emerging requirements for AI transparency and accountability. The findings advocate for a shift from opaque forecasting systems to transparent, interpretable frameworks that bridge the gap between technical accuracy and responsible AI governance, laying a robust foundation for future advancements in intelligent, ethical cloud resource management in dynamic digital commerce landscapes.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.36948/ijfmr.2025.v07i04.51796
- https://www.ijfmr.com/papers/2025/4/51796.pdf
- OA Status
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https://openalex.org/W4412741365Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.36948/ijfmr.2025.v07i04.51796Digital Object Identifier
- Title
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Explainable AI (XAI) for Cloud Resource Forecasting in E-Commerce EnvironmentsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-25Full publication date if available
- Authors
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S. Singh, V. Ravindran, S. H. PatilList of authors in order
- Landing page
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https://doi.org/10.36948/ijfmr.2025.v07i04.51796Publisher landing page
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https://www.ijfmr.com/papers/2025/4/51796.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://www.ijfmr.com/papers/2025/4/51796.pdfDirect OA link when available
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Cloud computing, Resource (disambiguation), Business, Computer science, Operating system, Computer networkTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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