Timestamp-Guided Knowledge Distillation for Robust Sensor-Based Time-Series Forecasting.

Area of Science:

• Data Science
• Machine Learning
• Time-Series Analysis

Background:

• Accurate time-series forecasting is crucial for sensor-driven applications like energy and traffic monitoring.
• Existing methods often neglect global temporal information from timestamps, limiting forecasting performance.
• Timestamps offer valuable, underutilized data for enhancing sensor-based predictions.

Purpose of the Study:

• To propose a novel timestamp-guided knowledge distillation framework (TKDF) for improved time-series forecasting.
• To integrate both historical and timestamp information for more robust predictions.
• To leverage mutual learning between heterogeneous prediction branches.

Main Methods:

• Developed a TKDF integrating a Backbone Model for local dependencies and a Timestamp Mapper for global temporal patterns.
• Employed a self-distillation mechanism within the Timestamp Mapper to enhance information transfer and reduce redundancy.
• Utilized mutual learning between complementary branches for improved forecasting robustness.

Main Results:

• The TKDF framework consistently improved the performance of mainstream forecasting models across diverse datasets.
• Experiments demonstrated the effectiveness of integrating timestamp information for enhanced forecasting.
• The proposed method showed significant performance gains in electricity consumption, traffic flow, and meteorological measurements.

Conclusions:

• The TKDF framework effectively utilizes global temporal information from timestamps to boost time-series forecasting accuracy.
• Integrating historical and timestamp data through knowledge distillation offers a robust approach for sensor-driven applications.
• The proposed method provides a valuable advancement for improving the reliability and performance of forecasting models.