{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:14:59Z","timestamp":1769634899243,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,15]]},"DOI":"10.1145\/3768292.3770402","type":"proceedings-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:24:26Z","timestamp":1763105066000},"page":"274-282","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["BForTFin: A Financial Domain-Aware Multiscale Evaluation Method for Time-Series Foundation Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8073-7877","authenticated-orcid":false,"given":"Nigel","family":"Cheong","sequence":"first","affiliation":[{"name":"NTU, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3668-191X","authenticated-orcid":false,"given":"Ling","family":"Wei Hsuen","sequence":"additional","affiliation":[{"name":"NTU, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0733-7381","authenticated-orcid":false,"given":"Satapathy","family":"Ranjan","sequence":"additional","affiliation":[{"name":"A*STAR-IHPC, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3030-1280","authenticated-orcid":false,"given":"Erik","family":"Cambria","sequence":"additional","affiliation":[{"name":"NTU, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9116-1595","authenticated-orcid":false,"given":"Rick","family":"Siow Mong Goh","sequence":"additional","affiliation":[{"name":"A*STAR-IHPC, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1910-8954","authenticated-orcid":false,"given":"Joyjit","family":"Chattoraj","sequence":"additional","affiliation":[{"name":"A*STAR-IHPC, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"e_1_3_3_2_2_2","unstructured":"Abdul\u00a0Fatir Ansari Lorenzo Stella Caner Turkmen Xiyuan Zhang Pedro Mercado Huibin Shen Oleksandr Shchur Syama\u00a0Sundar Rangapuram Sebastian\u00a0Pineda Arango Shubham Kapoor et\u00a0al. 2024. Chronos: Learning the language of time series. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2403.07815 (2024)."},{"key":"e_1_3_3_2_3_2","unstructured":"Andreas Auer Patrick Podest Daniel Klotz Sebastian B\u00f6ck G\u00fcnter Klambauer and Sepp Hochreiter. 2025. TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learning. arxiv:https:\/\/arXiv.org\/abs\/2505.23719\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2505.23719"},{"key":"e_1_3_3_2_4_2","unstructured":"Shaojie Bai J\u00a0Zico Kolter and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1803.01271 (2018)."},{"key":"e_1_3_3_2_5_2","unstructured":"Anastasia Borovykh Sander Bohte and Cornelis\u00a0W Oosterlee. 2017. Conditional time series forecasting with convolutional neural networks. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1703.04691 (2017)."},{"key":"e_1_3_3_2_6_2","volume-title":"1st ICML Workshop on Foundation Models for Structured Data","author":"Chitsaz Fatemeh","unstructured":"Fatemeh Chitsaz and Saman Haratizadeh. [n. d.]. Dual Adaptation of Time-Series Foundation Models for Financial Forecasting. In 1st ICML Workshop on Foundation Models for Structured Data."},{"key":"e_1_3_3_2_7_2","unstructured":"Robert\u00a0B Cleveland William\u00a0S Cleveland Jean\u00a0E McRae Irma Terpenning et\u00a0al. 1990. STL: A seasonal-trend decomposition. J. off. Stat 6 1 (1990) 3\u201373."},{"key":"e_1_3_3_2_8_2","volume-title":"Forty-first International Conference on Machine Learning","author":"Das Abhimanyu","year":"2024","unstructured":"Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. 2024. A decoder-only foundation model for time-series forecasting. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_3_2_9_2","unstructured":"Xinqi Dong Bo Dang Hengyi Zang Shaojie Li and Danqing Ma. 2024. The prediction trend of enterprise financial risk based on machine learning arima model. Journal of Theory and Practice of Engineering Science 4 01 (2024) 65\u201371."},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Wenzhi Feng Xiang Ma Xuemei Li and Caiming Zhang. 2023. A representation learning framework for stock movement prediction. Applied Soft Computing 144 (2023) 110409.","DOI":"10.1016\/j.asoc.2023.110409"},{"key":"e_1_3_3_2_11_2","first-page":"16115","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Goswami Mononito","year":"2024","unstructured":"Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski. 2024. MOMENT: a family of open time-series foundation models. In Proceedings of the 41st International Conference on Machine Learning. 16115\u201316152."},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Arthur Hoarau Arnaud Martin Jean-Christophe Dubois and Yolande Le\u00a0Gall. 2023. Evidential random forests. Expert Systems with Applications 230 (2023) 120652.","DOI":"10.1016\/j.eswa.2023.120652"},{"key":"e_1_3_3_2_13_2","unstructured":"Yifan Hu Yuante Li Peiyuan Liu Yuxia Zhu Naiqi Li Tao Dai Shu-tao Xia Dawei Cheng and Changjun Jiang. 2025. Fintsb: A comprehensive and practical benchmark for financial time series forecasting. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2502.18834 (2025)."},{"key":"e_1_3_3_2_14_2","volume-title":"Proceedings of ICDM Workshops","author":"Huang Zihao","year":"2025","unstructured":"Zihao Huang, Kelvin Du, Xulang Zhang, Rui Mao, and Erik Cambria. 2025. Combining LLM-Generated Knowledge Graphs with RAG for Financial Sentiment Extraction. In Proceedings of ICDM Workshops."},{"key":"e_1_3_3_2_15_2","unstructured":"Siva Rama\u00a0Krishna Kottapalli Karthik Hubli Sandeep Chandrashekhara Garima Jain Sunayana Hubli Gayathri Botla and Ramesh Doddaiah. 2025. Foundation Models for Time Series: A Survey. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2504.04011 (2025)."},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Heng\u00a0Yew Lee Woan\u00a0Lin Beh and Kong\u00a0Hoong Lem. 2023. Forecasting with information extracted from the residuals of ARIMA in financial time series using continuous wavelet transform. International Journal of Business Intelligence and Data Mining 22 1-2 (2023) 70\u201399.","DOI":"10.1504\/IJBIDM.2023.127313"},{"key":"e_1_3_3_2_17_2","unstructured":"Zhe Li Xiangfei Qiu Peng Chen Yihang Wang Hanyin Cheng Yang Shu Jilin Hu Chenjuan Guo Aoying Zhou Christian\u00a0S Jensen et\u00a0al. 2024. TSFM-Bench: A Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.11802 (2024)."},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671451"},{"key":"e_1_3_3_2_19_2","unstructured":"Yong Liu Guo Qin Zhiyuan Shi Zhi Chen Caiyin Yang Xiangdong Huang Jianmin Wang and Mingsheng Long. 2025. Sundial: A Family of Highly Capable Time Series Foundation Models. arxiv:https:\/\/arXiv.org\/abs\/2502.00816\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2502.00816"},{"key":"e_1_3_3_2_20_2","unstructured":"Ben\u00a0A Marconi. 2025. Time Series Foundation Models for Multivariate Financial Time Series Forecasting. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2507.07296 (2025)."},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"crossref","unstructured":"M Mohan PK Raja P Velmurugan and A Kulothungan. 2023. Holt-winters algorithm to predict the stock value using recurrent neural network. methods 8 10 (2023).","DOI":"10.32604\/iasc.2023.026255"},{"key":"e_1_3_3_2_22_2","unstructured":"Yuqi Nie Nam\u00a0H Nguyen Phanwadee Sinthong and Jayant Kalagnanam. 2022. A time series is worth 64 words: Long-term forecasting with transformers. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2211.14730 (2022)."},{"key":"e_1_3_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3672608.3707894"},{"key":"e_1_3_3_2_24_2","unstructured":"Kashif Rasul Arjun Ashok Andrew\u00a0Robert Williams Hena Ghonia Rishika Bhagwatkar Arian Khorasani Mohammad Javad\u00a0Darvishi Bayazi George Adamopoulos Roland Riachi Nadhir Hassen Marin Bilo\u0161 Sahil Garg Anderson Schneider Nicolas Chapados Alexandre Drouin Valentina Zantedeschi Yuriy Nevmyvaka and Irina Rish. 2024. Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting. arxiv:https:\/\/arXiv.org\/abs\/2310.08278\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2310.08278"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"crossref","unstructured":"David Salinas Valentin Flunkert Jan Gasthaus and Tim Januschowski. 2020. DeepAR: Probabilistic forecasting with autoregressive recurrent networks. International journal of forecasting 36 3 (2020) 1181\u20131191.","DOI":"10.1016\/j.ijforecast.2019.07.001"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1051\/matecconf\/202439201163"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"crossref","unstructured":"Shan Suthaharan and Shan Suthaharan. 2016. Support vector machine. Machine learning models and algorithms for big data classification: thinking with examples for effective learning (2016) 207\u2013235.","DOI":"10.1007\/978-1-4899-7641-3_9"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"crossref","unstructured":"Allan Timmermann and Clive\u00a0WJ Granger. 2004. Efficient market hypothesis and forecasting. International Journal of forecasting 20 1 (2004) 15\u201327.","DOI":"10.1016\/S0169-2070(03)00012-8"},{"key":"e_1_3_3_2_29_2","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.883"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.307"},{"key":"e_1_3_3_2_32_2","doi-asserted-by":"crossref","unstructured":"Frank Xing Kelvin Du Gianmarco Mengaldo Erik Cambria and Roy Welsch. 2025. AI Reshaping Financial Modeling. npj Artificial Intelligence 1 1 (2025).","DOI":"10.1038\/s44387-025-00030-w"},{"key":"e_1_3_3_2_33_2","unstructured":"Wei\u00a0Jie Yeo Ranjan Satapathy and Erik Cambria. 2024. Towards faithful natural language explanations: A study using activation patching in large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2410.14155 (2024)."},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSPC57692.2023.10125661"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3711507.3711508"},{"key":"e_1_3_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Jiawen Zhang Xumeng Wen Zhenwei Zhang Shun Zheng Jia Li and Jiang Bian. 2024. ProbTS: Benchmarking point and distributional forecasting across diverse prediction horizons. Advances in Neural Information Processing Systems 37 (2024) 48045\u201348082.","DOI":"10.52202\/079017-1523"},{"key":"e_1_3_3_2_37_2","volume-title":"The eleventh international conference on learning representations","author":"Zhang Yunhao","year":"2023","unstructured":"Yunhao Zhang and Junchi Yan. 2023. Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. In The eleventh international conference on learning representations."}],"event":{"name":"ICAIF '25: 6th ACM International Conference on AI in Finance","location":"Singapore Singapore","acronym":"ICAIF '25"},"container-title":["Proceedings of the 6th ACM International Conference on AI in Finance"],"original-title":[],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:25:36Z","timestamp":1763105136000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3768292.3770402"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":36,"alternative-id":["10.1145\/3768292.3770402","10.1145\/3768292"],"URL":"https:\/\/doi.org\/10.1145\/3768292.3770402","relation":{},"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"2025-11-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}