Demo: Explaining Time Series Interactively with CHRONOPROF

Abstract

Interpreting time series predictions from advanced Machine Learning and Deep Learning (ML/DL) models is challenging, as these models often function as black boxes, limiting their applicability in critical domains. To address this, we leverage CHRONOPROF, an Explainable AI (XAI) technique specifically designed for time series data, built upon the SHAP framework. CHRONOPROF improves interpretability by deriving virtual weights from SHAP values, offering a linearized representation of complex model decisions while preserving temporal coherence. However, CHRONOPROF’s complexity can pose challenges for non-expert users. To mitigate this, we developed an interactive dashboard that simplifies interpretation by retrieving stored data and SHAP values to compute and visualize virtual weights along with other representations that are derived from them. This user-friendly interface enables users to explore model behavior across different models and datasets. Ultimately, this innovation facilitates the adoption of CHRONOPROF and fosters trust in AI-driven network operations.