Spatio-temporal forecasting of future values of spatially correlated time
series is important across many cyber-physical systems (CPS). Recent studies
offer evidence that the use of graph neural networks to capture latent
correlations between time series holds a potential for enhanced forecasting.
However, most existing methods rely on pre-defined or self-learning graphs,
which are either static or unintentionally dynamic, and thus cannot model the
time-varying correlations that exhibit trends and periodicities caused by the
regularity of the underlying processes in CPS. To tackle such limitation, we
propose Time-aware Graph Structure Learning (TagSL), which extracts time-aware
correlations among time series by measuring the interaction of node and time
representations in high-dimensional spaces. Notably, we introduce time
discrepancy learning that utilizes contrastive learning with distance-based
regularization terms to constrain learned spatial correlations to a trend
sequence. Additionally, we propose a periodic discriminant function to enable
the capture of periodic changes from the state of nodes. Next, we present a
Graph Convolution-based Gated Recurrent Unit (GCGRU) that jointly captures
spatial and temporal dependencies while learning time-aware and node-specific
patterns. Finally, we introduce a unified framework named Time-aware Graph
Convolutional Recurrent Network (TGCRN), combining TagSL, and GCGRU in an
encoder-decoder architecture for multi-step spatio-temporal forecasting. We
report on experiments with TGCRN and popular existing approaches on five
real-world datasets, thus providing evidence that TGCRN is capable of advancing
the state-of-the-art. We also cover a detailed ablation study and visualization
analysis, offering detailed insight into the effectiveness of time-aware
structure learning.
Multidisciplinary Concepts in Spatio-Temporal Forecasting
Spatio-temporal forecasting involves predicting future values of time series data with spatial correlations. This concept is highly relevant across various cyber-physical systems (CPS), which are integrated systems that connect the physical world with computer-based systems. To improve forecasting accuracy, recent studies have explored the use of graph neural networks to capture latent correlations between time series.
However, most existing methods in this field rely on pre-defined or self-learning graphs, which are either static or unintentionally dynamic. This limitation prevents them from effectively modeling time-varying correlations that exhibit trends and periodicities caused by the regularity of underlying processes in CPS.
In response to this challenge, the Time-aware Graph Structure Learning (TagSL) approach is proposed. TagSL aims to extract time-aware correlations among time series by measuring the interaction of node and time representations in high-dimensional spaces. By considering the dynamics of correlations, TagSL addresses the need to model time-varying correlations accurately.
One key contribution of TagSL is the introduction of time discrepancy learning. This component utilizes contrastive learning with distance-based regularization terms to ensure that learned spatial correlations conform to a trend sequence. By incorporating this aspect, TagSL can capture the underlying trends and periodic changes exhibited by the time series data.
An additional novel component proposed in this study is the use of a periodic discriminant function. This function allows for the capture of periodic changes from the state of nodes in the graph. By considering periodicity, TagSL can better understand cyclic patterns that may repeat within the time series data.
To jointly capture spatial and temporal dependencies while learning time-aware and node-specific patterns, a Graph Convolution-based Gated Recurrent Unit (GCGRU) is introduced. This component combines graph convolutional operations with gated recurrent units, enabling effective modeling of both spatial and temporal relationships.
Finally, a unified framework called Time-aware Graph Convolutional Recurrent Network (TGCRN) is presented. TGCRN integrates TagSL and GCGRU in an encoder-decoder architecture designed specifically for multi-step spatio-temporal forecasting. By combining the strengths of both approaches, TGCRN aims to advance the state-of-the-art in this field.
Key Findings and Contributions
The authors conducted experiments using TGCRN and compared it with popular existing approaches on five real-world datasets. The results provide evidence that TGCRN outperforms these approaches, highlighting its capability to advance the state-of-the-art in spatio-temporal forecasting.
Additionally, the study includes a detailed ablation study and visualization analysis. These components offer valuable insights into the effectiveness of time-aware structure learning. By thoroughly analyzing the results, the authors provide a comprehensive evaluation of their proposed approach and validate its potential.
The multidisciplinary nature of this study is worth noting. It combines concepts from graph theory, neural networks, and time series analysis. The integration of these disciplines allows for a holistic approach to spatio-temporal forecasting and demonstrates the importance of interdisciplinary collaboration in advancing research and solving complex problems.
In conclusion, the proposed Time-aware Graph Convolutional Recurrent Network (TGCRN) presents a novel and comprehensive framework for multi-step spatio-temporal forecasting. By addressing the limitations of existing methods through time-aware structure learning and incorporating both spatial and temporal dependencies, TGCRN offers promising results and pushes the boundaries of the field.