MST and MPT: Lightweight Incremental Algorithms for Multivariate Anomaly Detection and Correction on TinyML Devices

The Internet of Things (IoT) generates massive multivariate time series data requiring real-time anomaly detection and correction for reliable monitoring. This challenges resource-constrained embedded devices due to conventional offline training and batch processing. To address this, we propose two algorithms derived from the TEDARLS framework: Multivariate Sequential TEDA (MST) and Multivariate Parallel TEDA (MPT). Derived from the TEDARLS framework, both enable on-device detection and correction of multivariate anomalies within TinyML constraints. A case study with real vehicular sensor data demonstrated low inference times and consistent embedded behavior. Supervised metrics were only assessed on synthetic data. MPT, though more sensitive, introduced greater signal distortions and required significantly longer processing times. Overall, MST demonstrated superior stability and suitability for real-time anomaly correction in resource-constrained IoT environments. This approach addresses an important gap in embedded analytics for IoT by enabling lightweight, accurate, and autonomous anomaly detection and correction at the edge.