DeepAI AI Chat
Log In Sign Up

Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding

05/26/2019
by   Doyup Lee, et al.
Statice
POSTECH
Kakao Corp.
0

Short-term demand forecasting models commonly combine convolutional and recurrent layers to extract complex spatiotemporal patterns in data. Long-term histories are also used to consider periodicity and seasonality patterns as time series data. In this study, we propose an efficient architecture, Temporal-Guided Network (TGNet), which utilizes graph networks and temporal-guided embedding. Graph networks extract invariant features to permutations of adjacent regions instead of convolutional layers. Temporal-guided embedding explicitly learns temporal contexts from training data and is substituted for the input of long-term histories from days/weeks ago. TGNet learns an autoregressive model, conditioned on temporal contexts of forecasting targets from temporal-guided embedding. Finally, our model achieves competitive performances with other baselines on three spatiotemporal demand dataset from real-world, but the number of trainable parameters is about 20 times smaller than a state-of-the-art baseline. We also show that temporal-guided embedding learns temporal contexts as intended and TGNet has robust forecasting performances even to atypical event situations.

READ FULL TEXT
09/03/2020

Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting

Modern energy systems collect high volumes of data that can provide valu...
06/21/2021

Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand

Electric vehicles can offer a low carbon emission solution to reverse ri...
07/22/2021

Tsformer: Time series Transformer for tourism demand forecasting

AI-based methods have been widely applied to tourism demand forecasting....
09/16/2021

Decoupling Long- and Short-Term Patterns in Spatiotemporal Inference

Sensors are the key to sensing the environment and imparting benefits to...
09/25/2020

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

Traditional methods for demand forecasting only focus on modeling the te...
10/03/2022

Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction

The continuous expansion of the urban construction scale has recently co...
03/08/2020

Progressive Growing of Neural ODEs

Neural Ordinary Differential Equations (NODEs) have proven to be a power...