Energy Efficient Streaming Time Series Classification with Attentive Power Iteration

Efficiently processing time series data streams in real-time on resource-constrained devices offers significant advantages in terms of enhanced computational energy efficiency and reduced time-related risks. We introduce an innovative streaming time series classification network that utilizes attentive power iteration, enabling real-time processing on resource-constrained devices. Our model continuously updates a compact representation of the entire time series, enhancing classification accuracy while conserving energy and processing time. Notably, it excels in streaming scenarios without requiring complete time series access, enabling swift decisions. Experimental results show that our approach excels in classification accuracy and energy efficiency, with over 70% less consumption and threefold faster task completion than benchmarks. This work advances real-time responsiveness, energy conservation, and operational effectiveness for constrained devices, contributing to optimizing various applications.

Authors

• Hao Huang Amazon Web Services
• Tapan Shah GE Vernova Research
• Scott Evans GE Vernova Research
• Shinjae Yoo

Keywords

• DMKM: Data Stream Mining
• DMKM: Mining of Spatial, Temporal or Spatio-Temporal Data
• ML: Deep Learning Algorithms
• ML: Time-Series/Data Streams