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State-of-the-art deep spatio-temporal predictive models depend on rich and representative training data for target regions and tasks. However, the availability of such data is typically limited. Furthermore, existing predictive models fail to utilize cross-correlations across tasks and cities. In this paper, we propose <jats:sc>MetaCitta<\/jats:sc>, a novel deep meta-learning approach that addresses the critical challenges of data scarcity and model generalization. <jats:sc>MetaCitta<\/jats:sc> adopts the data from different cities and tasks in a generalizable spatio-temporal deep neural network. We propose a novel meta-learning algorithm that minimizes the discrepancy between spatio-temporal representations across tasks and cities. Our experiments with real-world data demonstrate that the proposed <jats:sc>MetaCitta<\/jats:sc> approach outperforms state-of-the-art prediction methods for zero-shot learning and pre-training plus fine-tuning. Furthermore, <jats:sc>MetaCitta<\/jats:sc> is computationally more efficient than the existing meta-learning approaches.<\/jats:p>","DOI":"10.1007\/978-3-031-33383-5_6","type":"book-chapter","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T19:01:43Z","timestamp":1685386903000},"page":"70-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MetaCitta: Deep Meta-Learning for\u00a0Spatio-Temporal Prediction Across Cities and\u00a0Tasks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7049-3387","authenticated-orcid":false,"given":"Ashutosh","family":"Sao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2576-4640","authenticated-orcid":false,"given":"Simon","family":"Gottschalk","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0911-6264","authenticated-orcid":false,"given":"Nicolas","family":"Tempelmeier","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5134-9072","authenticated-orcid":false,"given":"Elena","family":"Demidova","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"6_CR1","unstructured":"Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. 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