Multi-View Neural Differential Equations for Continuous-Time Stream Data in Long-Term Traffic Forecasting

TL;DR

This paper introduces Multi-View Neural Differential Equations, a novel model that improves long-term traffic forecasting by capturing complex spatio-temporal patterns and delayed dynamics in continuous-time stream data.

Contribution

The paper presents a new NDE architecture that models multiple views of traffic data, addressing limitations of traditional NDEs in long-term predictions.

Findings

Outperforms state-of-the-art methods in long-term traffic forecasting

Demonstrates robustness to noisy and missing data

Effectively captures delayed and trend patterns in traffic data

Abstract

Long-term traffic flow forecasting plays a crucial role in intelligent transportation as it allows traffic managers to adjust their decisions in advance. However, the problem is challenging due to spatio-temporal correlations and complex dynamic patterns in continuous-time stream data. Neural Differential Equations (NDEs) are among the state-of-the-art methods for learning continuous-time traffic dynamics. However, the traditional NDE models face issues in long-term traffic forecasting due to failures in capturing delayed traffic patterns, dynamic edge (location-to-location correlation) patterns, and abrupt trend patterns. To fill this gap, we propose a new NDE architecture called Multi-View Neural Differential Equations. Our model captures current states, delayed states, and trends in different state variables (views) by learning latent multiple representations within Neural Differential Equations. Extensive experiments conducted on several real-world traffic datasets demonstrate that our proposed method outperforms the state-of-the-art and achieves superior prediction accuracy for long-term forecasting and robustness with noisy or missing inputs.