Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

Understand

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.

• While recent methods demonstrate good forecasting abilities, they have three fundamental limitations.
• (i) Discrete neural architectures: Interlacing individually parameterized spatial and temporal blocks to encode rich underlying patterns leads to discontinuous latent state trajectories and higher forecasting numerical errors.
• (ii) High complexity: Discrete approaches complicate models with dedicated designs and redundant parameters, leading to higher computational and memory overheads.