Multi-step Point-of-Interest-level Crowd Flow Prediction Based on Meta Learning

Author:

Feng Yuting1,Zhu Xinning1,Tang Xiaosheng1,Hu Zheng1

Affiliation:

1. Beijing University of Posts and Telecommunications Beijing, China, State Key Laboratory of Networking and Switching Technology, China

Funder

The National Key R&D Program of China

Publisher

ACM

Reference27 articles.

1. D. Chai L. Wang and Q. Yang. 2018. Bike Flow Prediction with Multi-Graph Convolutional Networks. ACM (2018). D. Chai L. Wang and Q. Yang. 2018. Bike Flow Prediction with Multi-Graph Convolutional Networks. ACM (2018).

2. Junyoung Chung Caglar Gulcehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014). Junyoung Chung Caglar Gulcehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014).

3. SSML: Self-Supervised Meta-Learner for En Route Travel Time Estimation at Baidu Maps

4. Chelsea Finn , Pieter Abbeel , and Sergey Levine . 2017 . Model-agnostic meta-learning for fast adaptation of deep networks . In International Conference on Machine Learning. PMLR, 1126–1135 . Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning. PMLR, 1126–1135.

5. DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks;Flunkert Valentin;International Journal of Forecasting,2020

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