Ensemble Learning with Physics-Informed Neural Networks for Harsh Time Series Analysis

In time series data analysis, particularly in dynamic environments like road traffic, the challenges posed by harsh conditions, nonlinearity, and stochasticity are formidable. This paper introduces a novel approach that synergizes Physics-Informed Neural Networks (PINNs) and Ensemble Transfer Learning (ETL) to address these challenges, enhancing the accuracy and reliability of time series analysis and prediction. PINNs, by incorporating domain knowledge through partial differential equations (PDEs), enable the integration of underlying physics principles into neural network architectures. This fusion of data-driven insights with physical constraints provides a robust framework for capturing complex relationships in time series data. ETL complements PINNs by leveraging multiple models trained on related datasets, enhancing generalization across scenarios and improving forecasting accuracy. A case study focusing on road traffic data is expected to demonstrate the effectiveness of this concept, utilizing real-world traffic data and encoding basic traffic flow equations with PINNs. The anticipated results suggest that the ensemble of PINNs with transfer learning will surpass traditional methods, exhibiting superior predictive capabilities and adaptability to dynamic conditions, even in unobserved scenarios.