Accepted Papers

2024

1. Oral

   Maven: A Multimodal Foundation Model for Supernova Science

   Gemma Zhang, Thomas Helfer, Alexander Thomas Gagliano, Siddharth Mishra-Sharma, and V Ashley Villar
2. Oral

   PaPaGei: Open Foundation Models for Optical Physiological Signals

   Arvind Pillai, Dimitris Spathis, Fahim Kawsar, and Mohammad Malekzadeh
3. Oral

   Partial Channel Dependence with Channel Masks for Time Series Foundation Model

   Seunghan Lee, Taeyoung Park, and Kibok Lee
4. Oral

   Scaling-laws for Large Time-series Models

   Justin Alsing, Thomas Edwards, Benjamin Dan Wandelt, James Alvey, and Nam H Nguyen
5. Oral

   Towards Time-Series Reasoning with LLMs

   Winnie Chow, Lauren E. Gardiner, Haraldur T Hallgrimsson, Maxwell A Xu, and Shirley You Ren
6. Spotlight

   Benchmarking out-of-the-box forecasters of varying scales in biology

   Anthony E Culos, and Mohammed AlQuraishi
7. Spotlight

   Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models

   Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien Siems, David Salinas, and Frank Hutter
8. Spotlight

   Time Series under Temporal Label Noise

   Sujay Nagaraj, Walter Gerych, Sana Tonekaboni, Anna Goldenberg, Berk Ustun, and Thomas Hartvigsen
9. Spotlight

   TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data

   Ege Onur Taga, Muhammed Emrullah Ildiz, and Samet Oymak
10. Spotlight

    TimeSeriesExam: A Time Series Understanding Exam

    Yifu Cai, Arjun Choudhry, Mononito Goswami, and Artur Dubrawski
11. Spotlight

    Towards Resolution-Aware Retrieval Augmented Zero-Shot Forecasting

    Iman Deznabi, Peeyush Kumar, and Madalina Fiterau
12. A Language Model-Guided Framework for Mining Time Series with Distributional Shifts

    Haibei Zhu, Yousef El-Laham, Elizabeth Fons, and Svitlana Vyetrenko
13. Adaptive Information Routing for Multi Modal Time Series Forecasting

    Jun Seo, Hyeokjun Choe, Seohui Bae, Soyeon Park, Jinseok Yang, Dongwan Kang, and Woohyung Lim
14. Align and Fine-Tune: Enhancing LLMs for Time-Series Forecasting

    Ching Chang, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen, and Sagar Samtani
15. Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models

    Divij Gupta, Anubhav Bhatti, and Surajsinh Parmar
16. Catching the Spikes: Heteroscedastic Uncertainty Quantification for Enhanced Malaria Prediction

    Feng Chen, Qi Qi, Jiayu Qiu, Kemeng Zhang, and Xiang Li
17. Context is Key: A Benchmark for Forecasting with Essential Textual Information

    Arjun Ashok, Andrew Robert Williams,  Marcotte, Valentina Zantedeschi, Jithendaraa Subramanian, Roland Riachi, James Requeima, Alexandre Lacoste, Irina Rish, Nicolas Chapados, and Alexandre Drouin
18. Critical Evaluation of Time Series Foundation Models in Demand Forecasting

    Santosh Kumar Puvvada, and Satyajit Chaudhuri
19. Deep Temporal Deaggregation: Large-Scale Spatio-Temporal Generative Models

    David Bergström, Mattias Tiger, and Fredrik Heintz
20. Do we really need Foundation Models for multi-step-ahead Epidemic Forecasting?

    Mrinmoy Dey, Aprameyo Chakrabartty, Dhruv Sarkar, and Tanujit Chakraborty
21. Domain-adapted Lag-Llama for Time Series Forecasting in the African Retail Sector.

    Kelian Massa, and Dario Fanucchi
22. Effectively Leveraging Exogenous Information across Neural Forecasters

    Andres Potapczynski, Kin G. Olivares, Malcolm Wolff, Andrew Gordon Wilson, Dmitry Efimov, and Vincent Quenneville-Belair
23. Efficient Time Series Processing for Transformers and State-Space Models through Token Merging

    Leon Götz, Marcel Kollovieh, Stephan Günnemann, and Leo Schwinn
24. Electrocardiogram Report Generation and Question Answering via Retrieval-Augmented Self-Supervised Modeling

    Jialu Tang, Tong Xia, Yuan Lu, Cecilia Mascolo, and Aaqib Saeed
25. Enhance Time Series Modeling by Integrating LLM

    Can Chen, Gabriel L. Oliveira, Hossein Sharifi-Noghabi, and Tristan Sylvain
26. Enhancing Multivariate Time Series Forecasting via Multi-Task Learning and Random Matrix Theory

    Romain Ilbert, Malik Tiomoko, Cosme Louart, Vasilii Feofanov, Themis Palpanas, and Ievgen Redko
27. Fine-Tuning a Time Series Foundation Model with Wasserstein Loss

    Andrei Chernov
28. From RNNs to Foundation Models: An Empirical Study on Commercial Building Energy Consumption

    Shourya Bose, Yijiang Li, Amy Van Sant, Yu Zhang, and Kibaek Kim
29. GIFT-Eval: A Benchmark for General Time Series Forecasting Model Evaluation

    Taha Aksu, Gerald Woo, Juncheng Liu, Xu Liu, Chenghao Liu, Silvio Savarese, Caiming Xiong, and Doyen Sahoo
30. General-Purpose Brain Foundation Models for Time-Series Neuroimaging Data

    Mohammad Javad Darvishi Bayazi, Hena Ghonia, Roland Riachi, Bruno Aristimunha, Arian Khorasani, Md Rifat Arefin, Amin Darabi, Guillaume Dumas, and Irina Rish
31. Generalized Prompt Tuning: How to Use a Frozen Pre-Trained Univariate Time Series Foundation Model for Multivariate Time Series Prediction

    Mingzhu Liu, Angela Chen, and George H. Chen
32. Hierarchical Time Series Forecasting Via Latent Mean Encoding

    Alessandro Salatiello, Stefan Birr, and Manuel Kunz
33. Implicit Reasoning in Deep Time Series Forecasting

    Willa Potosnak, Cristian Ignacio Challu, Mononito Goswami, Michał Wiliński, Nina Żukowska, and Artur Dubrawski
34. In-context Quantile Regression for Multi-product Inventory Management using Time-series Transformers

    Magnus Josef Maichle, Sohom Mukherjee, Kai Günder, Ivane Antonov, Nikolai Stein, and Richard Pibernik
35. Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series

    Maxx Richard Rahman, Ruoxuan Liu, and Wolfgang Maass
36. Joint Embedding go Temporal

    Sofiane Ennadir, Siavash Golkar, and Leopoldo Sarra
37. KAN4Drift: Are KAN Effective for Identifying and Tracking Concept Drift in Time Series?

    Kunpeng Xu, Lifei Chen, and Shengrui Wang
38. LETS-C: Leveraging Text Embedding for Time Series Classification

    Rachneet Kaur, Zhen Zeng, Tucker Balch, and Manuela Veloso
39. LLMForecaster: Improving Seasonal Event Forecasts with Unstructured Textual Data

    Hanyu Zhang, Chuck Arvin, Dmitry Efimov, Michael W. Mahoney, Dominique Perrault-Joncas, Shankar Ramasubramanian, Andrew Gordon Wilson, and Malcolm Wolff
40. Leveraging Periodicity for Robustness with Multi-modal Mood Pattern Models

    Jaya Narain, Qinhua Jenny Sun, Oussama Elachqar, Haraldur T Hallgrimsson, Feng Zhu, and Shirley You Ren
41. LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration

    Camiel Oerlemans, Bram Grooten, Michiel Braat, Alaa Alassi, Emilia Silvas, and Decebal Constantin Mocanu
42. MEDS-torch: An ML Pipeline for Inductive Experiments for EHR Medical Foundation Models

    Nassim Oufattole, Teya Bergamaschi, Pawel Renc, Aleksia Kolo, Matthew B.A. McDermott, and Collin Stultz
43. Masking the Gaps: An Imputation-Free Approach to Time Series Modeling with Missing Data

    Abhilash Neog, Arka Daw, Sepideh Fatemi Khorasgani, and Anuj Karpatne
44. Measuring Pre-training Data Quality without Labels for Time Series Foundation Models

    Songkang Wen, Vasilii Feofanov, and Jianfeng Zhang
45. Mixture of Experts for Time Series Foundation Models

    Xu Liu, Juncheng Liu, Gerald Woo, Taha Aksu, Chenghao Liu, Silvio Savarese, Caiming Xiong, and Doyen Sahoo
46. Optimizing Time Series Forecasting Architectures: A Hierarchical Neural Architecture Search Approach

    Difan Deng, and Marius Lindauer
47. PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision

    Arnav Mohanty Das, Chi Ian Tang, Fahim Kawsar, and Mohammad Malekzadeh
48. Preventing Conflicting Gradients in Neural Temporal Point Process Models for Irregular Time Series Data

    Tanguy Bosser, and Souhaib Ben Taieb
49. Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models the Answer?

    Young-Jin Park, François Germain, Jing Liu, Ye Wang, Gordon Wichern, Toshiaki Koike-Akino, Navid Azizan, Christopher R. Laughman, and Ankush Chakrabarty
50. Reimagining Time Series Foundation Models: Metadata and State-Space Model Perspectives

    Pengrui Quan, Ozan Baris Mulayim, Liying Han, Dezhi Hong, Mario Berges, and Mani Srivastava
51. Revisiting Masked Auto-Encoders for ECG-Language Representation Learning

    Manh Pham Hung, Aaqib Saeed, and Dong Ma
52. Scaling to Billion Parameters for Time Series Foundation Models with Mixture of Experts

    Xiaoming Shi, Shiyu Wang, Yuqi Nie, Dianqi Li, Zhou Ye, Qingsong Wen, and Ming Jin
53. Sequential Order-Robust Mamba for Time Series Forecasting

    Seunghan Lee, Juri Hong, Kibok Lee, and Taeyoung Park
54. Stochastic Sparse Sampling: A Framework for Local Explainability in Variable-Length Medical Time Series

    Xavier Mootoo, Alan Arnoldo Diaz Montiel, Milad Lankarany, and Hina Tabassum
55. Test-Time Learning For Time Series Forecasting

    Panayiotis Christou, Shichu Chen, Xupeng Chen, and Parijat Dube
56. Text2Freq: Learning Series Patterns from Text via Frequency Domain

    Ming-Chih Lo, Ching Chang, and Wen-Chih Peng
57. The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features

    Shi Bin Hoo, Samuel Müller, David Salinas, and Frank Hutter
58. Towards Large-scale Clinical Multi-variate Time-series Datasets

    Manuel Burger, Fedor Sergeev, Malte Londschien, Daphné Chopard, Hugo Yèche, Eike Christian Gerdes, Polina Leshetkina, Alexander Morgenroth, Zeynep Babür, Jasmina Bogojeska, Martin Faltys, Rita Kuznetsova, and Gunnar Ratsch
59. Towards Long-Context Time Series Foundation Models

    Nina Żukowska, Mononito Goswami, Michał Wiliński, Willa Potosnak, and Artur Dubrawski
60. Towards Unbiased Evaluation of Time-series Anomaly Detector

    Debarpan Bhattacharya, Sumanta Mukherjee, Chandramouli Kamanchi, Vijay Ekambaram, Arindam Jati, and Pankaj Dayama
61. TrajGPT: Healthcare Time-Series Representation Learning for Trajectory Prediction

    Ziyang Song, Qincheng Lu, He Zhu, David L. Buckeridge, and Yue Li
62. Transformer-based Time-Series Biomarker Discovery for COPD Diagnosis

    Soham Gadgil, Joshua Mark Galanter, and Mohammadreza Negahdar
63. UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting

    Juncheng Liu, Chenghao Liu, Gerald Woo, Yiwei Wang, Bryan Hooi, Caiming Xiong, and Doyen Sahoo
64. Unveiling and Manipulating Concepts in Time Series Foundation Models

    Michał Wiliński, Mononito Goswami, Nina Żukowska, Willa Potosnak, and Artur Dubrawski
65. Unveiling the Potential of Text in High-Dimensional Time Series Forecasting

    Xin Zhou, Weiqing Wang, SHILIN QU, Zhiqiang Zhang, and Christoph Bergmeir
66. Weakly-supervised Multi-sensor Anomaly Detection with Time-series Foundation Models

    Zelin He, Matthew Reimherr, Sarah Alnegheimish, and Akash Chandrayan
67. When Larger Isn’t Better: Lightweight CNNs Outperform Large Time-Series Models in Classification of Oil and Gas Drilling Data

    benzine, J.S. Buiting, Soumyadipta Sengupta, Badal Gupta, and Youssef Tamaazousti
68. Zero shot time series forecasting using Kolgomorov Arnold Networks

    Abhiroop Bhattacharya, and Nandinee Haq
69. ♠ SPADE ♠ Split Peak Attention DEcomposition

    Malcolm Wolff, Kin G. Olivares, Boris N. Oreshkin, Sunny Ruan, Sitan Yang, Abhinav Katoch, Shankar Ramasubramanian, Youxin Zhang, Michael W. Mahoney, Dmitry Efimov, and Vincent Quenneville-Belair