Multi-Source Knowledge-Based Hybrid Neural Framework for Time Series Representation Learning

Sagar Srinivas Sakhinana, Krishna Sai Sudhir Aripirala, Shivam Gupta, Venkataramana Runkana

Keywords: Deep Learning on Graphs, Time Series Representation Learning, Knowledge-Based Compositional Generalization

Abstract:

Accurately predicting the behavior of complex dynamical systems, characterized by high-dimensional multivariate time
series(MTS) in interconnected sensor networks, is crucial for informed decision-making in various applications to minimize risk. While graph forecasting networks(GFNs) are ideal for forecasting MTS data that exhibit spatio-temporal dependencies, prior works rely solely on the domain-specific knowledge of time-series variables inter-relationships to model the nonlinear dynamics, neglecting inherent relational-structural dependencies among the variables within the MTS data. In contrast, contemporary works infer relational structures from MTS data but neglect domain-specific knowledge. The proposed hybrid architecture addresses these limitations by combining both domain-specific knowledge and implicit knowledge of the relational structure underlying the MTS data using Knowledge-Based Compositional Generalization. The hybrid architecture shows promising results on multiple benchmark datasets, outperforming state-of-the-art forecasting methods. Additionally, the architecture models the time-varying uncertainty of multi-horizon forecasts.