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Deep train-from-scratch models have exhibited effective performance yet require large amounts of data, which limits real-world applicability. Recently, researchers have leveraged the representation learning transferability of pre-trained Large Language Models (LLMs) to handle limited non-linguistic datasets effectively. However, incorporating LLMs with time-series data presents challenges of limited adaptation due to different compositions between time-series and linguistic data, and the inability to process multi-scale temporal information. To tackle these challenges, we propose LLM4TS, a framework for time-series forecasting with pre-trained LLMs. LLM4TS consists of a two-stage fine-tuning strategy: the\n                    <jats:italic>time-series alignment<\/jats:italic>\n                    stage to align LLMs with the nuances of time-series data and the\n                    <jats:italic>forecasting fine-tuning<\/jats:italic>\n                    stage for downstream time-series forecasting tasks. Furthermore, our framework features a novel two-level aggregation method that integrates multi-scale temporal data within pre-trained LLMs, enhancing their ability to interpret time-specific information. In experiments across seven time-series forecasting datasets, LLM4TS is superior to existing state-of-the-art methods compared with trained-from-scratch models in full-shot scenarios and also achieves the highest rank in few-shot scenarios. In addition, evaluations compared with different unsupervised representation learning approaches highlight LLM4TS\u2019s effectiveness with representation learning in forecasting tasks. Ablation studies further validate each component\u2019s contribution to LLM4TS and underscore the essential role of utilizing LLM\u2019s pre-trained weights for optimal performance. The code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/blacksnail789521\/LLM4TS\">https:\/\/github.com\/blacksnail789521\/LLM4TS<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3719207","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:46:39Z","timestamp":1740134799000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":107,"title":["LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5031-5487","authenticated-orcid":false,"given":"Ching","family":"Chang","sequence":"first","affiliation":[{"name":"Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6551-1720","authenticated-orcid":false,"given":"Wei-Yao","family":"Wang","sequence":"additional","affiliation":[{"name":"Computational Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8964-0933","authenticated-orcid":false,"given":"Wen-Chih","family":"Peng","sequence":"additional","affiliation":[{"name":"Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6925-893X","authenticated-orcid":false,"given":"Tien-Fu","family":"Chen","sequence":"additional","affiliation":[{"name":"Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"issue":"3","key":"e_1_3_1_2_2","first-page":"331","article-title":"A comparative study of demand forecasting based on machine learning methods with time series approach","volume":"9","author":"Bonab Akbar Abbaspour Ghadim","year":"2022","unstructured":"Akbar Abbaspour Ghadim Bonab. 2022. 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