HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting

09/28/2018
by   Zheyi Pan, et al.
0

Spatio-temporal (ST) data, which represent multiple time series data corresponding to different spatial locations, are ubiquitous in real-world dynamic systems, such as air quality readings. Forecasting over ST data is of great importance but challenging as it is affected by many complex factors, including spatial characteristics, temporal characteristics and the intrinsic causality between them. In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models. More specifically, it consists of three major modules: a spatial module, a temporal module and a deduction module. Among them, the deduction module derives the parameter weights of the temporal module from the spatial characteristics, which are extracted by the spatial module. Then, we design a general form of HyperST layer as well as different forms for several basic layers in neural networks, including the dense layer (HyperST-Dense) and the convolutional layer (HyperST-Conv). Experiments on three types of real-world tasks demonstrate that the predictive models integrated with our framework achieve significant improvements, and outperform the state-of-the-art baselines as well.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/29/2022

Towards Spatio-Temporal Aware Traffic Time Series Forecasting–Full Version

Traffic time series forecasting is challenging due to complex spatio-tem...
research
04/23/2018

Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery

We introduce a dynamical spatio-temporal model formalized as a recurrent...
research
07/15/2020

On the Inclusion of Spatial Information for Spatio-Temporal Neural Networks

When confronting a spatio-temporal regression, it is sensible to feed th...
research
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...
research
02/22/2023

Prediction of single well production rate in water-flooding oil fields driven by the fusion of static, temporal and spatial information

It is very difficult to forecast the production rate of oil wells as the...
research
09/16/2023

Test-Time Compensated Representation Learning for Extreme Traffic Forecasting

Traffic forecasting is a challenging task due to the complex spatio-temp...
research
05/12/2020

Localized convolutional neural networks for geospatial wind forecasting

Convolutional Neural Networks (CNN) possess many positive qualities when...

Please sign up or login with your details

Forgot password? Click here to reset