Machine Learning-Based Outlier Detection for Business Intelligence: A Scalable Time Series Analysis Framework
Year: 2024
Type: article
Abstract: The exponential growth in digital business operations has resulted in an unprecedented volume of time series data generated from diverse business metrics, creating an urgent need for sophisticated anomaly detection systems. This paper presents a comprehensive framework for detecting outliers in business time series data using advanced machine learning techniques, addressing the challenges of scale, accuracy, and real-time processing. We propose a novel hybrid approach that seamlessly integrates statistical methods with deep learning architectures to identify both point anomalies and pattern deviations in multivariate business metrics. The framework incorporates adaptive thresholding mechanisms and contextual awareness, leveraging business domain knowledge to reduce false positives while maintaining high detection accuracy across varying business cycles and seasonal patterns. Our approach addresses the challenges of scalability and real-time processing through a sophisticated distributed computing architecture, making it suitable for enterprise-scale deployments. The framework demonstrates superior performance in handling concept drift, seasonal variations, and complex interdependencies between metrics, while maintaining computational efficiency and interpretability. Keywords—anomaly detection, business intelligence, machine learning, time series analysis, deep learning, statistical methods, distributed computing, real-time processing, adaptive thresholding, feature engineering
DOI: 10.55041/ijsrem6716
Authors: Discover Financial Services Illinois, USA, Anirudh Reddy Pathe
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405003376