Work

GMT: Gzip-based Memory-efficient Time-series classification

Year: 2024

Type: article

Abstract: The deployment of embedded time-series sensing devices enabled better understanding of user environments and contexts. However, classifying them solely on extremely limited devices under data-scarce conditions is still a remaining challenge. We introduce GMT , a memory-efficient parameter-free classifier that uses gzip compressor and k -nearest neighbors ( k NN) for classifying multi-channel time-series data. GMT tackles issues due to high data fidelity, multi-channel characteristics, and numerical properties of sensor data using techniques such as floating point quantization , channel-wise compression , and hybrid distance . Experiments show that GMT provides superior accuracy and memory efficiency compared to other classifiers across various tasks and applications.

Source: ICT Express

DOI: 10.1016/j.icte.2024.12.003

Authors: Sungmin Lee, Kichang Lee, JaeYeon Park, JeongGil Ko

Institutions: Yonsei University, Dankook University, Pohang University of Science and Technology

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,500

Funders: Malta Council for Science and Technology, Korea Creative Content Agency, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion