MLPST: MLP is All You Need for Spatio-Temporal Prediction-Reference-Cited by-同舟云学术

MLPST: MLP is All You Need for Spatio-Temporal Prediction

Published:2023-10-21 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
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Author:

Zhang Zijian¹^ORCID,Huang Ze²^ORCID,Hu Zhiwei²^ORCID,Zhao Xiangyu³^ORCID,Wang Wanyu³^ORCID,Liu Zitao⁴^ORCID,Zhang Junbo⁵^ORCID,Qin S. Joe⁶^ORCID,Zhao Hongwei¹^ORCID

Affiliation:

1. Jilin University, Changchun, China

2. City University of Hong Kong, Hong Kong, China

3. City University of Hong Kong, Hong Kong, Hong Kong

4. Jinan University, Guangzhou, China

5. JD Intelligent Cities Research & JD iCity, JD Technology, Beijing, China

6. Lingnan University, Hong Kong, Hong Kong

Funder

Shenzhen-Hong Kong-Macau Science and Technology Project Category C

Natural Science Foundation of Jilin Province

CityU - HKIDS Early Career Research Grant

Tencent

Natural Science Foundation of China

Fundamental Research Funds for the Central Universities, JLU

Hong Kong ITC Innovation and Technology Fund Midstream Research Programme for Universities Project

Beijing Nova Program

Provincial Science and Technology Innovation Special Fund Project of Jilin Province

Huawei

Kuaishou

Key Laboratory of Smart Education of Guangdong Higher Education Institutes, Jinan University

National Natural Science Foundation of China

Beijing Natural Science Foundation

Collaborative Research Fund by RGC of Hong Kong

National Key R&D Program of China

APRC - CityU New Research Initiatives

SIRG - CityU Strategic Interdisciplinary Research Grant

Ant Group

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3583780.3614969

Reference57 articles.

1. Spatio-Temporal Data Mining

2. Lei Bai Lina Yao Salil Kanhere Xianzhi Wang Quan Sheng etal 2019. Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting. arXiv preprint arXiv:1905.10069 (2019). Lei Bai Lina Yao Salil Kanhere Xianzhi Wang Quan Sheng et al. 2019. Stg2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting. arXiv preprint arXiv:1905.10069 (2019).

3. Bike flow prediction with multi-graph convolutional networks

4. PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion Prediction

5. Shoufa Chen , Enze Xie , Chongjian Ge , Ding Liang , and Ping Luo . 2021 . Cyclemlp: A mlp-like architecture for dense prediction. arXiv preprint arXiv:2107.10224 (2021). Shoufa Chen, Enze Xie, Chongjian Ge, Ding Liang, and Ping Luo. 2021. Cyclemlp: A mlp-like architecture for dense prediction. arXiv preprint arXiv:2107.10224 (2021).