Accepted Papers

Our proceedings can be found on OpenReview.

Selected for an Oral Presentation

  • Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models – Ethan Baron, Boris N. Oreshkin, Ruijun Ma, Hanyu Zhang, Kari Torkkola, Michael W. Mahoney, Andrew Gordon Wilson, Tatiana Konstantinova
  • Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification – Andreas Auer, Daniel Klotz, Sebastian Böck, Sepp Hochreiter
  • FlowState: Sampling-Rate Invariant Time Series Foundation Model with Dynamic Forecasting Horizons – Lars Graf, Thomas Ortner, Stanisław Woźniak, Angeliki Pantazi
  • CHRONOGRAPH: A Real-World Graph-Based Multivariate Time Series Dataset – Luțu Adrian-Cătălin, Ioana Pintilie, Andrei Manolache, Elena Burceanu

Accepted from the First Call

  • qHuBERT: Quantized Model for ECG Classification – Tushar Shinde, Sudhanshu Gaurhar, Anil Kumar Tiwari

  • OATS: Online Data Augmentation for Time Series Foundation Models – Junwei Deng, Chang Xu, Jiaqi W. Ma, Jiang Bian

  • TempusBench: An Evaluation Framework for Time-Series Forecasting – Denizalp Goktas, Amy Greenwald, Gerardo Riano-Briceno, Alexandra Magnusson, Alif Abdullah, Beatriz de Lucio

  • Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction – Echo Diyun Lu, Charles SM Findling, Marianne Clausel, Alessandro Leite, Wei Gong, Pierric Kersaudy

  • Zero-shot forecasting of epidemics – Madhurima Panja, Ojas Modak, Grace Younes, Tanujit Chakraborty

  • Linear Regression as a Litmus Test for Time Series Forecasting Benchmarks – Walid Bouainouche, Gabriel Singer, Vasilii Feofanov, Ievgen Redko

  • Leveraging Generic Time Series Foundation Models for EEG Classification – Theo Gnassounou, Yessin Moakher, Shifeng Xie, Vasilii Feofanov

  • Goal-Oriented Time-Series Forecasting: Foundation Framework Design – Tareq Si Salem, Luca-Andrei Fechete, Mohamed SANA, Fadhel Ayed, Antonio De Domenico, Nicola Piovesan, Wenjie Li

  • On the Internal Semantics of Time-Series Foundation Models – Atharva Pandey, Abhilash Neog, Gautam Jajoo

  • Zero-to-Forecast: Natural Language to Time Series Prediction via Cross-Modal Ensembles – Gokul Srinath Seetha Ram

  • FlowState: Sampling-Rate Invariant Time Series Foundation Model with Dynamic Forecasting Horizons – Lars Graf, Thomas Ortner, Stanisław Woźniak, Angeliki Pantazi

  • HORIZON: A Benchmark for In-the-wild User Behaviour Modeling – Arnav Goel, Pranjal A Chitale, Bhawna Paliwal, Bishal Santra, Amit Sharma

  • TimeCopilot – Azul Garza, Renee Rosillo Garcia

  • LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting – Yu-Neng Chuang, Songchen Li, Jiayi Yuan, Guanchu Wang, Kwei-Herng Lai, Joshua Han, Zihang Xu, Songyuan Sui, Leisheng Yu, Sirui Ding, Chia-Yuan Chang, Alfredo Costilla Reyes, Daochen Zha, Xia Hu

  • Zero Shot Time Series Forecasting: Do Time Series FMs Outperform Cross Modal FMs? – Md Younus Ahamed, Md Asif Bin Syed

  • TimeSeriesExamAgent: Creating TimeSeries Reasoning Benchmarks at Scale – Malgorzata Gwiazda, Yifu Cai, Mononito Goswami, Artur Dubrawski

  • LLM-Integrated Bayesian State Space Models for Multimodal Time-Series Forecasting – Sungjun Cho, Changho Shin, Suenggwan Jo, Xinya Yan, Shourjo Aditya Chaudhuri, Frederic Sala

  • FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines – Hetvi Shastri, Pragya Sharma, Walid A. Hanafy, Mani Srivastava, Prashant Shenoy

  • TimeSqueeze: Dynamic Patching for Efficient Time Series Forecasting – Sravan Kumar Ankireddy, Nikita Seleznev, Nam H Nguyen, Yulun Wu, Senthil Kumar, Furong Huang, C. Bayan Bruss

  • Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift – Tianze Wang, Sofiane ENNADIR, John Pertoft, Gabriela Zarzar Gandler, Lele Cao, Zineb Senane, Styliani Katsarou, Sahar Asadi, Axel Karlsson, Oleg Smirnov

  • Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers – Simon Roschmann, Quentin Bouniot, Zeynep Akata

  • Adaptive Refinement of Time Series Foundation Models via Pattern and Context-Awareness – Ashish Mishra, Tarun Kumar, Satish Kumar Mopur, Sergey Serebryakov, Suparna Bhattacharya, Martin Foltin, Ramanagopal Vogety, Phanidhar Koganti

  • CHRONOGRAPH: A Real-World Graph-Based Multivariate Time Series Dataset – Luțu Adrian-Cătălin, Ioana Pintilie, Andrei Manolache, Elena Burceanu

  • A More Realistic Evaluation of Cross-Frequency Transfer Learning and Foundation Forecasting Models – Kin G. Olivares, Malcolm Wolff, Tatiana Konstantinova, Shankar Ramasubramanian, Boris N. Oreshkin, Andrew Gordon Wilson, Ravi Kiran Vadlamani, Andres Potapczynski, Willa Potosnak, Michael W. Mahoney, Mengfei Cao, Dmitry Efimov

  • TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning – Junru Zhang, Lang Feng, Xu Guo, Yuhan Wu, Yabo Dong, Duanqing Xu

  • LiveDrill: Multimodal Segment-Triggered Data-to-Text for Time Series Foundation Models – Soumyadipta Sengupta, Amine EL KHAIR, Sebastiaan Buiting, Imane Khaouja, Yahia Salaheldin Shaaban, Abdallah Benzine

  • Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models – Ethan Baron, Boris N. Oreshkin, Ruijun Ma, Hanyu Zhang, Kari Torkkola, Michael W. Mahoney, Andrew Gordon Wilson, Tatiana Konstantinova

  • Kronos: A Foundation Model for the Language of Financial Markets – Yu Shi, Zongliang Fu, Shuo Chen, Bohan Zhao, Wei Xu, Changshui Zhang, Jian Li

  • Beyond Naïve Prompting: Strategies for Improved Zero-shot Context-aided Forecasting with LLMs – Arjun Ashok, Andrew Robert Williams, Vincent Zhihao Zheng, Irina Rish, Nicolas Chapados, Étienne Marcotte, Valentina Zantedeschi, Alexandre Drouin

  • How Foundational are Foundation Models for Time Series Forecasting? – Nouha KARAOULI, Denis Coquenet, Elisa Fromont, Martial Mermillod, Marina Reyboz

  • Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification – Andreas Auer, Daniel Klotz, Sebastian Böck, Sepp Hochreiter

  • Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative – Zihao Li, Xiao Lin, Zhining Liu, Jiaru Zou, Ziwei Wu, Lecheng Zheng, Dongqi Fu, Yada Zhu, Hendrik Hamann, Hanghang Tong, Jingrui He

  • time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models – Debdeep Sanyal, Aaryan Nagpal, Dhruv Kumar, Murari Mandal, Saurabh Deshpande

Accepted from the Second Call

  • Time is Low Rank: Understanding the Compressibility of Transformers for Time Series – Annan Yu, Danielle C. Maddix, Boran Han, Xiyuan Zhang, Abdul Fatir Ansari, Oleksandr Shchur, Christos Faloutsos, Andrew Gordon Wilson, Michael W. Mahoney, Yuyang Wang

  • Decoding Time Series Foundation Models with Three Inductive Biases – Annan Yu, Danielle C. Maddix, Boran Han, Xiyuan Zhang, Abdul Fatir Ansari, Oleksandr Shchur, Christos Faloutsos, Andrew Gordon Wilson, Michael W. Mahoney, Yuyang Wang

  • Are Time-Indexed Foundation Models the Future of Time Series Imputation ? – Etienne Le Naour, Tahar Nabil, Adrien Petralia, Ghislain Agoua

  • RAVE: Relevance-Aligned Validation of Explanations for Zero-Shot LLM-Based Time Series Analysis – Mehdi Zakaria ADJAL, Faïcel Chamroukhi

  • Uncovering Zero-Shot Generalization Gaps in Time-Series Foundation Models Using Real World Videos – Lujun Li, Lama Sleem, Yiqun Wang, Yangjie Xu, Niccolò Gentile, Radu State

  • Likelihood-Aligned Forecast Networks – Denizalp Goktas, Gerardo Riano, Amy Greenwald

  • There is No “apple” in Timeseries: Rethinking TSFM through the Lens of Invariance – Arian Prabowo, Flora D. Salim