Schoenegger et al. (2024): AI Forecasting

This paper addresses a critical challenge in machine learning: distribution shift in multi-sensor time series data. The authors propose SMORE, an innovative domain adaptation algorithm leveraging hyperdimensional computing (HDC) to handle out-of-distribution samples more efficiently than traditional deep learning methods. By dynamically constructing test-time models that consider domain context, SMORE provides a lightweight and adaptable solution for edge computing platforms. The methodology is particularly noteworthy for its unique approach to encoding multi-sensor time series data and constructing domain-specific models. By using HDC's parallel and efficient operations, SMORE achieves significant improvements in both accuracy and computational efficiency. Experimental results demonstrate an average 1.98% higher accuracy than state-of-the-art domain adaptation algorithms, with 18.81x faster training and 4.63x faster inference. The approach is especially promising for resource-constrained edge devices, where traditional deep learning models struggle with computational limitations.