VisCI: A visualization framework for anomaly detection and interactive optimization of composite index

Abstract

Composite index is always derived with the weighted aggregation of hierarchical components, which is widely utilized to distill intricate and multidimensional matters in economic and business statistics. However, the composite indices always present inevitable anomalies at different levels oriented from the calculation and expression processes of hierarchical components, thereby impairing the precise depiction of specific economic issues. In this paper, we propose VisCI, a visualization framework for anomaly detection and interactive optimization of composite index. First, LSTM-AE model is performed to detect anomalies from the lower level to the higher level of the composite index. Then, a comprehensive array of visual cues is designed to visualize anomalies, such as hierarchy and anomaly visualization. In addition, an interactive operation is provided to ensure accurate and efficient index optimization, mitigating the adverse impact of anomalies on index calculation and representation. Finally, we implement a visualization framework with interactive interfaces, facilitating both anomaly detection and intuitive composite index optimization. Case studies based on real-world datasets and expert interviews are conducted to demonstrate the effectiveness of our VisCI in commodity index anomaly exploration and anomaly optimization.