Predictive spatio-temporal modelling with neural networks
Liu, Hong-Bin (2020) Predictive spatio-temporal modelling with neural networks. PhD thesis, James Cook University.
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Abstract
Hongbin Liu studied the predictive spatio-temporal modelling using Neural Networks. Predictive spatio-temporal modelling is a challenge task due to the complex non-linear spatio-temporal dependencies, data sparsity and uncertainty.
Hongbin Liu investigated the modelling difficulties and proposed three novel models to tackle the difficulties for three common spatio-temporal datasets. He also conducted extensive experiments on several real-world datasets for various spatio-temporal prediction tasks, such as travel mode classification, next-location prediction, weather forecasting and meteorological imagery prediction. The results show our proposed models consistently achieve exceptional improvements over state-of-the-art baselines.
| Item ID: | 69130 |
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| Item Type: | Thesis (PhD) |
| Keywords: | trajectory classification, spatio-temporal trajectory, GRU, travel model classification, deep learning, transportation mode, LSTM, Recurrent Neural Network |
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| Copyright Information: | Copyright © 2020 Hong-Bin Liu. |
| Additional Information: | Four publications arising from this thesis are stored in ResearchOnline@JCU, at the time of processing. Please see the Related URLs. The publications are: Chapter 4: Liu, Hongbin, and Lee, Ickjai (2017) End-to-end trajectory transportation mode classification using Bi-LSTM recurrent neural network. In: Proceedings of the 12th International Conference on Intelligent Systems and Knowledge Engineering. 158. From: ISKE 2017: 12th International Conference on Intelligent Systems and Knowledge Engineering, 24-26 November 2017, Nanjing, China. Chapter 4: Liu, Hong-Bin, Wu, Hao, Sun, Weiwei, and Lee, Ickjai (2019) Spatio-temporal GRU for trajectory classification. In: Proceedings of the 19th IEEE International Conference on Data Mining. pp. 1228-1233. From: ICDM 2019: 19th IEEE International Conference on Data Mining, 8-11 November 2019, Beijng, China. Chapter 5: Liu, Hongbin, and Lee, Ickjai (2020) Bridging the gap between training and inference for spatio-temporal forecasting. In: Frontiers in Artificial Intelligence and Applications (325) pp. 1316-1323. From: ECAI 2020: 24th European Conference on Artificial Intelligence, 29 August - 8 September 2020, Santiago, Spain. Chapter 6: Liu, Hong-Bin, and Lee, Ickjai (2020) Towards realistic meteorological predictive learning using conditional GAN. IEEE Access, 8. pp. 93179-93186. |
| Date Deposited: | 31 Aug 2021 00:31 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 80% 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 20% |
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