DAM: Towards A Foundation Model for Time Series Forecasting
School of Computer Science, University of St Andrews（圣安德鲁斯大学计算机学院）；Systems Infrastructure Research（系统基础设施研究机构）；Edinburgh Research Centre（爱丁堡研究中心）；Central Software Institute（中央软件研究院）；Huawei（华为）
Luke Darlow, Qiwen Deng, Ahmed Hassan, Martin Asenov, Rajkarn Singh, Artjom Joosen, Adam Barker, Amos Storkey
It is challenging to scale time series forecasting models such that they forecast accurately for multiple distinct domains and datasets, all with potentially different underlying collection procedures (e.g., sample resolution), patterns (e.g., periodicity), and prediction requirements (e.g., reconstruction vs. forecasting). We call this general task universal forecasting. Existing methods usually assume that input data is regularly sampled, and they forecast to pre-determined horizons, resulting in failure to generalise outside of the scope of their training. We propose the DAM - a neural model that takes randomly sampled histories and outputs an adjustable basis composition as a continuous function of time for forecasting to non-fixed horizons. It involves three key components: (1) a flexible approach for using randomly sampled histories from a long-tail distribution, that enables an efficient global perspective of the underlying temporal dynamics while retaining focus on the recent history; (2) a transformer backbone that is trained on these actively sampled histories to produce, as representational output, (3) the basis coefficients of a continuous function of time. We show that a single univariate DAM, trained on 25 time series datasets, either outperformed or closely matched existing SoTA models at multivariate long-term forecasting across 18 datasets, including 8 held-out for zero-shot transfer, even though these models were trained to specialise for each dataset-horizon combination. This single DAM excels at zero-shot transfer and very-long-term forecasting, performs well at imputation, is interpretable via basis function composition and attention, can be tuned for different inference-cost requirements, is robust to missing and irregularly sampled data {by design}.
Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model
School of Engineering and Technology, China University of Geosciences (Beijing)（工程与技术学院，中国地质大学（北京））；School of Geological Engineering and Geomatics, Chang'an University（地质工程与测绘学院，长安大学）
How Foundational are Foundation Models for Time Series Forecasting?
Univ. Rennes（里昂大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化技术研究所）；IRISA - UMR 6074（国际信息与自动化研究所）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Univ. Savoie Mont Blanc（萨瓦伊-蒙布莱大学）；LPNC（实验室）；CEA（法国原子能委员会）；LIST（法国国家信息与自动化技术研究所）
TimeFound: A Foundation Model for Time Series Forecasting
Baidu Research（百度研究院）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；National University of Singapore（新加坡国立大学）；Salesforce AI Research（Salesforce AI研究部）；Squirrel AI（松鼠AI）
Prediction and Causality of functional MRI and synthetic signal using a Zero-Shot Time-Series Foundation Model
AGH University of Krakow, Poland（克拉科夫AGH大学）；Institut de Neurosciences de la Timone UMR 7289, Aix Marseille Université, CNRS, 13005, Marseille, France（里莫内神经科学研究所）
Leveraging Exogenous Signals for Hydrology Time Series Forecasting
College of Computing（计算学院）；Georgia Institute of Technology（佐治亚理工学院）；University of Virginia（弗吉尼亚大学）；School of Data Science（数据科学学院）；Computer Science（计算机科学）；Biocomplexity Institute（生物复杂性研究所）
Pre-trained Forecasting Models: Strong Zero-Shot Feature Extractors for Time Series Classification
NXAI GmbH（NXAI公司）；ELLIS Unit, LIT AI Lab, Institute for Machine Learning, JKU Linz（ELLIS单位、LIT AI实验室、机器学习研究所）；Interdisciplinary Transformation University Austria（跨学科转型大学奥地利）
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Morgan Stanley（摩根士丹利）；ServiceNow Research（ServiceNow 研究院）；Université de Montréal（蒙特利尔大学）；Mila-Quebec AI Institute（米拉-魁北克人工智能研究所）；McGill University（麦吉尔大学）
Evaluation of a Foundational Model and Stochastic Models for Forecasting Sporadic or Spiky Production Outages of High-Performance Machine Learning Services
QKCV Attention: Enhancing Time Series Forecasting with Static Categorical Embeddings for Both Lightweight and Pre-trained Foundation Models
Independent Researcher（独立研究者）；Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai)（信息行为脑机智能关键实验室）；School of Business and Management（商学院）
Xaurora: Generative Weather Forecasting with Denoising Stochastic Interpolants from a Foundation Model Prior
Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Amsterdam（阿姆斯特丹大学）；Delft University of Technology（代尔夫特理工大学）；Centrum Wiskunde & Informatica（数学与计算机科学中心）
Distilling Time Series Foundation Models for Efficient Forecasting
The City College of New York, City University of New York, USA（纽约城市学院，纽约城市大学）；Institute of Computing Technology, Chinese Academy of Sciences, China（中国科学院计算技术研究所）；Stevens Institute of Technology, USA（史蒂文斯理工学院）
Research Institute for Infrastructure Paradigm Shift, Yachiyo Engineering Co., Ltd.（基础设施范式转换研究所，八千代工程株式会社）
最近，由于气候变化引起的前所未有的天气条件，洪水灾害已成为一个社会问题。对暴雨立即作出反应对于减轻经济损失和迅速恢复都很重要。时空降水预报可以提高大坝入库流量预测的准确性，提前6小时以上减轻洪水损失。然而，由于目标预测值与地面实况值之间存在不可约的偏差，普通的ConvLSTM在实际降水预报中存在着可预报范围大于3个时间步的局限性。本文提出了一种雨码的时空降水代码到代码的预测方法。我们提出了一种新的雨的功能，表示一个时间雨的过程，使用多帧融合的时间步长减少。我们基于标准ConvLSTM对各种术语范围进行了雨码研究。我们应用了一个坝区内的日本雨季小时降水数据，2006年至2019年下约12.7万小时，每年5月至10月。我们将雷达分析每小时的数据应用于面积为136 x 148 km 2的中央更广泛的区域。最后，我们在几个预报范围内提供了雨码大小和每小时准确度之间的敏感性研究。
FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting
Julius-Maximilians-Universität Würzburg, Modeling and Simulation Lab（乌尔姆-马克斯·普朗克大学，建模与仿真实验室）；Weihenstephan-Triesdorf University of Applied Sciences, Smart Farming（魏因施泰因-特里尔夫应用科学大学，智能农业）；Weihenstephan-Triesdorf University of Applied Sciences, Digital Energy Transition（魏因施泰因-特里尔夫应用科学大学，数字能源转型）
RefineBridge: Generative Bridge Models Improve Financial Forecasting by Foundation Models
Department of Electrical and Electronic Engineering, Imperial College London, UK（帝国理工学院伦敦分校电子与电气工程系）；Department of CST, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）
