A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting

by   Hongjie Chen, et al.

Traditional methods for demand forecasting only focus on modeling the temporal dependency. However, forecasting on spatio-temporal data requires modeling of complex nonlinear relational and spatial dependencies. In addition, dynamic contextual information can have a significant impact on the demand values, and therefore needs to be captured. For example, in a bike-sharing system, bike usage can be impacted by weather. Existing methods assume the contextual impact is fixed. However, we note that the contextual impact evolves over time. We propose a novel context integrated relational model, Context Integrated Graph Neural Network (CIGNN), which leverages the temporal, relational, spatial, and dynamic contextual dependencies for multi-step ahead demand forecasting. Our approach considers the demand network over various geographical locations and represents the network as a graph. We define a demand graph, where nodes represent demand time-series, and context graphs (one for each type of context), where nodes represent contextual time-series. Assuming that various contexts evolve and have a dynamic impact on the fluctuation of demand, our proposed CIGNN model employs a fusion mechanism that jointly learns from all available types of contextual information. To the best of our knowledge, this is the first approach that integrates dynamic contexts with graph neural networks for spatio-temporal demand forecasting, thereby increasing prediction accuracy. We present empirical results on two real-world datasets, demonstrating that CIGNN consistently outperforms state-of-the-art baselines, in both periodic and irregular time-series networks.


Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting

Forecasting the number of visits to Points-of-Interest (POI) in an urban...

Long-term Spatio-temporal Forecasting via Dynamic Multiple-Graph Attention

Many real-world ubiquitous applications, such as parking recommendations...

AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs

We present the first whiteness test for graphs, i.e., a whiteness test f...

Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy

A key problem in location-based modeling and forecasting lies in identif...

HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting

Spatio-temporal (ST) data, which represent multiple time series data cor...

Multimodal Temporal Fusion Transformers Are Good Product Demand Forecasters

Multimodal demand forecasting aims at predicting product demand utilizin...

Please sign up or login with your details

Forgot password? Click here to reset