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time-series-11

Spatio-Temporal Forecasting

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Libraries

Use these libraries to find spatio-temporal-forecasting-11 models and implementations

benedekrozemberczki/pytorch_geometr…

2 papers 2,471

Datasets

Weather2K

Beijing Traffic

Subtasks

Human Behavior Forecasting

Most implemented papers

Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Yaguang Li, Rose Yu, C. Shahabi, Yan Liu•Wed Jul 05 2017

Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow and evaluates the framework on two real-world large scale road network traffic datasets and observes consistent improvement.

3698

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Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

Lina Yao, Xianzhi Wang, Lei Bai, Can Li, Can Wang•Sun Jul 05 2020

It is argued that learning node-specific patterns is essential for traffic forecasting while the pre-defined graph is avoidable, and two adaptive modules for enhancing Graph Convolutional Network (GCN) with new capabilities are proposed.

1710 0

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Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting

Amir Ghaderi, B. M. Sanandaji, Faezeh Ghaderi•Sun Jul 23 2017

Results of a case study on recorded time series data from a collection of wind mills in the north-east of the U.S. show that the proposed DL-based forecasting algorithm significantly improves the short-term forecasts compared to a set of widely-used benchmarks models.

114 0

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Multivariate Time-series Anomaly Detection via Graph Attention Network

Defu Cao, Yunhai Tong, Bixiong Xu, Yujing Wang, Congrui Huang, Jie Tong, Qi Zhang, Hang Zhao, Juanyong Duan, Jing Bai•Thu Sep 03 2020

This paper proposes a novel self-supervised framework for multivariate time-series anomaly detection that outperforms other state-of-the-art models on three real-world datasets and has good interpretability and is useful for anomaly diagnosis.

621 0

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Prediction-based One-shot Dynamic Parking Pricing

Noseong Park, Seoyoung Hong, H. Shin, Jeongwhan Choi•Mon Aug 29 2022

This work uses state-of-the-art deep learning technologies such as neural ordinary differential equations (NODEs) to design a one-shot price optimization method given a pre-trained prediction model, which requires only one iteration to find the optimal solution.

5 0

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Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning

Yong Yu, Junbo Zhang, Yu Zheng, Yuxuan Liang, Zheyi Pan, Weifeng Wang•Wed Jul 24 2019

This work proposed a deep-meta-learning based model, entitled ST-MetaNet, to collectively predict traffic in all location at once, consisting of a recurrent neural network to encode the traffic, a meta graph attention network to capture diverse spatial correlations, and a meta recurrent Neural network to consider diverse temporal correlations.

595 0

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Deep Integro-Difference Equation Models for Spatio-Temporal Forecasting

A. Zammit‐Mangion, C. Wikle•Mon Oct 28 2019

A deep convolution neural network in a hierarchical statistical IDE framework, where the CNN is designed to extract process dynamics from the process' most recent behaviour provides a global prior model for the dynamics that is realistic, interpretable, and computationally efficient.

49 0

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Graph Neural Networks for Improved El Niño Forecasting

Salomey Osei, Salva Rühling Cachay, Emma Erickson, A. Bucker, Ernest Pokropek, Willa Potosnak, Björn Lütjens•Tue Dec 01 2020

The application of spatiotemporal Graph Neural Networks (GNN) to forecast ENSO at long lead times, finer granularity and improved predictive skill than current state-of-the-art methods is proposed.

16 0

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Spatio-Temporal Forecasting | State-of-the-Art