ADAPTIVE SPATIAL-TEMPORAL INCEPTION GRAPH CONVOLUTION NETWORKS FOR MULTI-STEP SPATIAL-TEMPORAL DATA FORECASTING

Abstract

Spatial-temporal data forecasting is of great importance for industries such as telecom network operation and transportation management. However, spatialtemporal data are inherent with complex spatial-temporal correlations and behaves heterogeneities among the spatial and temporal aspects, which makes the forecasting remain as a very challenging task though recently great work has been done. In this paper, we propose a novel model, Adaptive Spatial-Temporal Inception Graph Convolution Networks (ASTI-GCN), to solve the multi-step spatial-temporal data forecasting problem. The model proposes multi-scale spatial-temporal joint graph convolution block to directly model the spatial-temporal joint correlations without introducing elaborately constructed mechanisms. Moreover inception mechanism combined with the graph node-level attention is introduced to make the model capture the heterogeneous nature of the graph adaptively. Our experiments on three real-world datasets from two different fields consistently show ASTI-GCN outperforms the state-of-the-art performance. In addition, ASTI-GCN is proved to generalize well.

1. INTRODUCTION

Spatial-temporal data forecasting has attracted attention from researchers due to its wide range of applications and the same specific characteristics of spatial-temporal data. Typical applications include mobile traffic forecast (He et al., 2019) , traffic road condition forecast (Song et al., 2020; Yu et al., 2017; Guo et al., 2019; Zheng et al., 2020; Li et al., 2017) , on-demand vehicle sharing services passenger demand forecast (Bai et al., 2019) and geo-sensory time series prediction (Liang et al., 2018) etc. The accurate forecast is the foundation of many real-world applications, such as Intelligent Telecom Network Operation and Intelligent Transportation Systems (ITS). Specifically, accurate traffic forecast can help transportation agencies better control traffic scheduling and reduce traffic congestion; The traffic volumes prediction of the wireless telecommunication network plays an important role for the network operation and optimization, for example, it can help to infer the accurate sleep periods (low traffic periods) of the base stations to achieve energy saving without sacrificing customer experience. However, as we all know, accurate spatialtemporal data forecasting faces multiple challenges. First, it is inherent with complex spatial-temporal correlations. In the spatialtemporal graph, different neighbors may have different impacts on the central location at the same time step, as the bold lines shown in Fig- ure1 , which called spatial correlations.Different historical observations of the same location influence the future moments of itself variously due to temporal correlations. The observations of different neighbors at historical moments can directly affect the central node at future time steps due to the spatial-temporal joint correlations. As shown in Figure1, the information of the spatialtemporal network can propagate along the spatial and temporal dimensions simultaneously, and the



Figure 1: Spatial-temporal correlations.

