ROCKET-LRP: Explainable Time Series Classification with Application to Anomaly Prediction in Manufacturing

Time Series Classification is a popular approach in machine learning with many applications. The Random Convolutional Kernel Transform (ROCKET) model has achieved state-of-the-art performance in various time-series classification tasks due to its ability to capture complex patterns and temporal relationships. However, its reliance on random convolutions hinders the explainability of the model, as the relationships between the transformed features and the original input data become obscured. To address these challenges, we propose a novel approach for computing explanations in ROCKET-based time-series classification models that integrates Layer-wise Relevance Propagation with either model-agnostic post-hoc or model-intrinsic local explanation techniques. We implement our approach for two widely used classification models and three local explanation techniques. We validate our approach on two simulated datasets, demonstrating its faithfulness and effectiveness. Additionally, we present an application of our approach to anomaly prediction in real-world manufacturing data and show that it provides superior local explanations compared to popular explanation techniques such as SHAP and LIME.