SMORE: Similarity-based Hyperdimensional Domain Adaptation for Multi-Sensor Time Series Classification

arXiv:2402.13233v1 Announce Type: new
Abstract: Many real-world applications of the Internet of Things (IoT) employ machine learning (ML) algorithms to analyze time series information collected by interconnected sensors. However, distribution shift, a fundamental challenge in data-driven ML, arises when a model is deployed on a data distribution different from the training data and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) are required to capture intricate spatial and temporal dependencies in multi-sensor time series data, often …

abstract algorithms analyze applications arxiv challenge classification cs.lg data data-driven distribution domain domain adaptation information internet internet of things iot machine machine learning sensor sensors series shift time series type world