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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2304.14343 |
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| _version_ | 1866910356515848192 |
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| author | Jiang, Jiawei Han, Chengkai Jiang, Wenjun Zhao, Wayne Xin Wang, Jingyuan |
| author_facet | Jiang, Jiawei Han, Chengkai Jiang, Wenjun Zhao, Wayne Xin Wang, Jingyuan |
| contents | As deep learning technology advances and more urban spatial-temporal data accumulates, an increasing number of deep learning models are being proposed to solve urban spatial-temporal prediction problems. However, there are limitations in the existing field, including open-source data being in various formats and difficult to use, few papers making their code and data openly available, and open-source models often using different frameworks and platforms, making comparisons challenging. A standardized framework is urgently needed to implement and evaluate these methods. To address these issues, we propose LibCity, an open-source library that offers researchers a credible experimental tool and a convenient development framework. In this library, we have reproduced 65 spatial-temporal prediction models and collected 55 spatial-temporal datasets, allowing researchers to conduct comprehensive experiments conveniently. By enabling fair model comparisons, designing a unified data storage format, and simplifying the process of developing new models, LibCity is poised to make significant contributions to the spatial-temporal prediction field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_14343 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction Jiang, Jiawei Han, Chengkai Jiang, Wenjun Zhao, Wayne Xin Wang, Jingyuan Machine Learning As deep learning technology advances and more urban spatial-temporal data accumulates, an increasing number of deep learning models are being proposed to solve urban spatial-temporal prediction problems. However, there are limitations in the existing field, including open-source data being in various formats and difficult to use, few papers making their code and data openly available, and open-source models often using different frameworks and platforms, making comparisons challenging. A standardized framework is urgently needed to implement and evaluate these methods. To address these issues, we propose LibCity, an open-source library that offers researchers a credible experimental tool and a convenient development framework. In this library, we have reproduced 65 spatial-temporal prediction models and collected 55 spatial-temporal datasets, allowing researchers to conduct comprehensive experiments conveniently. By enabling fair model comparisons, designing a unified data storage format, and simplifying the process of developing new models, LibCity is poised to make significant contributions to the spatial-temporal prediction field. |
| title | LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2304.14343 |