{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T07:19:21Z","timestamp":1779175161452,"version":"3.51.4"},"reference-count":12,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,8]]},"abstract":"<jats:p>Time Series Generation (TSG) is essential in many industries for generating synthetic data that mirrors real-world characteristics. TSGBench has advanced the field by offering comprehensive evaluations and unique insights for selecting suitable TSG methods. However, translating these advancements to industry applications is hindered by a cognitive gap among professionals and the absence of a dynamic platform for method comparison and evaluation. To address these issues, we introduce TSGAssist, an interactive assistant that integrates the strengths of TSGBench and harnesses Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for TSG recommendations and benchmarking. Our demonstration highlights its effectiveness in (1) enhancing TSG understanding, (2) providing industry-specific recommendations, and (3) offering a comprehensive benchmarking platform, illustrating its potential to ease industry professionals' navigation through the TSG landscape and encourage broader application across industries.<\/jats:p>","DOI":"10.14778\/3685800.3685862","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T17:25:21Z","timestamp":1731086721000},"page":"4309-4312","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["TSGAssist: An Interactive Assistant Harnessing LLMs and RAG for Time Series Generation Recommendations and Benchmarking"],"prefix":"10.14778","volume":"17","author":[{"given":"Yihao","family":"Ang","sequence":"first","affiliation":[{"name":"National University of Singapore, NUS Research Institute in Chongqing"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Bao","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Huang","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony K. H.","family":"Tung","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Huang","sequence":"additional","affiliation":[{"name":"National University of Singapore, NUS Research Institute in Chongqing"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Alex James Chan, and Mihaela van der Schaar","author":"Alaa Ahmed M.","year":"2021","unstructured":"Ahmed M. Alaa, Alex James Chan, and Mihaela van der Schaar. 2021. Generative Time-series Modeling with Fourier Flows. In ICLR."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/3632093.3632097"},{"key":"e_1_2_1_3_1","volume-title":"Anthony KH Tung, and Zhiyong Huang","author":"Ang Yihao","year":"2023","unstructured":"Yihao Ang, Qiang Huang, Anthony KH Tung, and Zhiyong Huang. 2023. A Stitch in Time Saves Nine: Enabling Early Anomaly Detection with Correlation Analysis. In ICDE. 1832--1845."},{"key":"e_1_2_1_4_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. In NeurIPS. 1877--1901."},{"key":"e_1_2_1_5_1","unstructured":"Abhyuday Desai Cynthia Freeman Zuhui Wang and Ian Beaver. 2021. TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation. arXiv:2111.08095"},{"key":"e_1_2_1_6_1","unstructured":"Plotly Technologies Inc. 2021. Dash. https:\/\/plotly.com\/dash\/."},{"key":"e_1_2_1_7_1","unstructured":"James Jordon Jinsung Yoon and Mihaela Van Der Schaar. 2018. PATE-GAN: Generating synthetic data with differential privacy guarantees. In ICLR."},{"key":"e_1_2_1_8_1","unstructured":"Daesoo Lee Sara Malacarne and Erlend Aune. 2023. Vector Quantized Time Series Generation with a Bidirectional Prior Model. In AISTATS. 7665--7693."},{"key":"e_1_2_1_9_1","unstructured":"Patrick Lewis Ethan Perez Aleksandra Piktus Fabio Petroni Vladimir Karpukhin Naman Goyal Heinrich K\u00fcttler Mike Lewis Wen-tau Yih Tim Rockt\u00e4schel et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. In NeurIPS. 9459--9474."},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Hengzhi Pei Kan Ren Yuqing Yang Chang Liu Tao Qin and Dongsheng Li. 2021. Towards generating real-world time series data. In ICDM. 469--478.","DOI":"10.1109\/ICDM51629.2021.00058"},{"key":"e_1_2_1_12_1","volume-title":"Ng","author":"Seyfi Ali","year":"2022","unstructured":"Ali Seyfi, Jean-Fran\u00e7ois Rajotte, and Raymond T. Ng. 2022. Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN). In NeurIPS. 32777--32788."},{"key":"e_1_2_1_13_1","unstructured":"Jinsung Yoon Daniel Jarrett and Mihaela van der Schaar. 2019. Time-series Generative Adversarial Networks. In NeurIPS. 5509--5519."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3685800.3685862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T05:27:27Z","timestamp":1735622847000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3685800.3685862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":12,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["10.14778\/3685800.3685862"],"URL":"https:\/\/doi.org\/10.14778\/3685800.3685862","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"2024-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}