Meta-Learning Task Relations for Ensemble-Based Temporal Domain Generalization in Sensor Data Forecasting.

Area of Science:

• Sensor data analysis
• Time series forecasting
• Machine learning

Background:

• Sensor data is non-stationary and evolves, causing distribution shifts.
• Single models struggle to generalize across varying temporal dynamics (scale, semantics, structure).
• Conflicts arise between domain-specific patterns and model capacity, hindering universal parameter learning.

Purpose of the Study:

• To propose an ensemble learning framework for improved temporal domain generalization in sensor data forecasting.
• To address challenges posed by distribution shifts and varying temporal dynamics in sensor time series.
• To enhance the generalization performance of forecasting models across unseen sensor segments.

Main Methods:

• Segmenting sensor time series into distinct temporal tasks.
• Applying a meta-learning strategy with a recurrent encoder and variational inference for shared representations.
• Modeling task relationships using a self-attention mechanism.
• Adaptively reweighting prediction results from multiple domain-specific models.

Main Results:

• The proposed ensemble learning framework significantly enhances generalization performance.
• The method effectively handles distribution shifts inherent in sensor measurements.
• Experiments on public datasets demonstrate superior forecasting accuracy across unseen sensor segments.

Conclusions:

• Ensemble learning combined with meta-learning offers a robust solution for temporal domain generalization in sensor data forecasting.
• The framework effectively captures and leverages shared representations across diverse temporal tasks.
• This approach improves the reliability and accuracy of sensor data forecasting in evolving environments.