GMT: Gzip-based Memory-efficient Time-series classification
Sungmin Lee, Ki‐Chang Lee, Jaeyeon Park, JeongGil Ko · ICT Express · 2024
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.