{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tcube-domain-agnostic-neural-time-series","title":"TCube: Domain-Agnostic Neural Time-series Narration","arxiv_id":"2110.05633","date":"2021-10-11","proceeding":null,"authors":["Mandar Sharma","John S. Brownstein","Naren Ramakrishnan"],"abstract":"The task of generating rich and fluent narratives that aptly describe the characteristics, trends, and anomalies of time-series data is invaluable to the sciences (geology, meteorology, epidemiology) or finance (trades, stocks, or sales and inventory). The efforts for time-series narration hitherto are domain-specific and use predefined templates that offer consistency but lead to mechanical narratives. We present TCube (Time-series-to-text), a domain-agnostic neural framework for time-series narration, that couples the representation of essential time-series elements in the form of a dense knowledge graph and the translation of said knowledge graph into rich and fluent narratives through the transfer-learning capabilities of PLMs (Pre-trained Language Models). TCube's design primarily addresses the challenge that lies in building a neural framework in the complete paucity of annotated training data for time-series. The design incorporates knowledge graphs as an intermediary for the representation of essential time-series elements which can be linearized for textual translation. To the best of our knowledge, TCube is the first investigation of the use of neural strategies for time-series narration. Through extensive evaluations, we show that TCube can improve the lexical diversity of the generated narratives by up to 65.38% while still maintaining grammatical integrity. The practicality and deployability of TCube is further validated through an expert review (n=21) where 76.2% of participating experts wary of auto-generated narratives favored TCube as a deployable system for time-series narration due to its richer narratives. Our code-base, models, and datasets, with detailed instructions for reproducibility is publicly hosted at https://github.com/Mandar-Sharma/TCube.","url_abs":"https://arxiv.org/abs/2110.05633v1","url_pdf":"https://arxiv.org/pdf/2110.05633v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tcube-domain-agnostic-neural-time-series","repo_url":"https://github.com/Mandar-Sharma/TCube","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"epidemiology","task_name":"Epidemiology"},{"task_slug":"knowledge-graphs","task_name":"Knowledge Graphs"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}