Mining Temporal Association Rules for Multivariate Time Series Classification Problems With Both Discrete and Continuous Values Based on Shapelets

Due to its excellent interpretability and high accuracy, time series Shapelet has garnered widespread attention in time series classification tasks. However, currently prevalent Shapelet classification methods primarily focus on numerical data, neglecting the common occurrence of categorical feature variables in practical applications. Additionally, existing multivariate time series classification algorithms exhibit short-comings in executing classification tasks following the Shapelet discovery process. Inspired by association rule mining, this paper proposes an innovative Shapelet classification algorithm aimed at addressing both numerical and categorical data in multivariate time series. This algorithm employs a unified representation method to effectively integrate categorical and continuous features, while enhancing existing time series Shapelet discovery methods by independently calculating Shapelets for each variable, making them more suitable for association rule mining. Leveraging the discovered Shapelets and Allen's interval relations, the algorithm constructs temporal relationships among multivariate time series Shapelets, enabling the discovery of frequent patterns and the completion of classification tasks. This study aims to fully leverage the interpretability of time series Shapelets, revealing hidden temporal patterns within time series data. Experimental results demonstrate that this algorithm outperforms existing benchmark algorithms for multivariate time series classification in terms of accuracy, while exhibiting significant interpretability advantages.