Enhancing Cloud Cost Forecasting with Explainable Artificial Intelligence

Cloud computing enables efficient digital transformations for organizations but also raises significant challenges for cost management due to its variability and complexity. Rapid advancements in Artificial Intelligence (AI) bring promising opportunities to address these challenges, particularly in cloud cost forecasting. However, implementing AI-based models for cloud cost forecasting remains novel and challenging, as the financial domain requires high trustworthiness in AI solutions. Explainable AI (XAI) addresses this issue by developing techniques that clarify AI decisions, making models more transparent and reliable. Moreover, XAI explanations can help identify redundant features, leading to improved model performance. This paper introduces a cloud cost forecasting approach using forecasting models for time series data. The predictions are explained using the Kernel SHAP method, which highlights the impact of different features on the model's output. The forecasting model is then refined by removing low-impact features. The results demonstrate that the refined models enhanced by XAI outperform the original models due to an efficient feature selection process. Our study highlights the capability of AI and XAI to address cloud cost forecasting challenges by providing accurate predictions and clear explanations.