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Given a forecasting task, which includes a dataset and a forecasting horizon, automated design methods automatically search for an optimal forecasting model for the task in a manually designed search space, and then train the identified model using the dataset to enable the forecasting. Existing automated methods face three challenges. First, the search space is constructed by human experts, rending the methods only semi-automated and yielding search spaces prone to subjective biases. Second, it is time consuming to search for an optimal model. Third, training the identified model for a new task is also costly. These challenges limit the practicability of automated methods in real-world settings. To contend with the challenges, we propose a fully automated and highly efficient correlated time series forecasting framework where the search and training can be done in minutes. The framework includes a data-driven, iterative strategy to automatically prune a large search space to obtain a high-quality search space for a new forecasting task. It includes a zero-shot search strategy to efficiently identify the optimal model in the customized search space. And it includes a fast parameter adaptation strategy to accelerate the training of the identified model. Experiments on seven benchmark datasets offer evidence that the framework is capable of state-of-the-art accuracy and is much more efficient than existing methods.<\/jats:p>","DOI":"10.14778\/3705829.3705835","type":"journal-article","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T23:21:06Z","timestamp":1740784866000},"page":"144-157","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Fully Automated Correlated Time Series Forecasting in Minutes"],"prefix":"10.14778","volume":"18","author":[{"given":"Xinle","family":"Wu","sequence":"first","affiliation":[{"name":"Aalborg University, Denmark"}]},{"given":"Xingjian","family":"Wu","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Dalin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aalborg University, Denmark"}]},{"given":"Miao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, China"}]},{"given":"Chenjuan","family":"Guo","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"East China Normal University, China"}]},{"given":"Christian S.","family":"Jensen","sequence":"additional","affiliation":[{"name":"Aalborg University, Denmark"}]}],"member":"320","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"17804","article-title":"Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting","volume":"33","author":"Bai Lei","year":"2020","unstructured":"Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. 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