AI 中文总结
TimeNet提出开源统一数据标准，解耦时间数据与任务定义，支持多模态异构数据集联合训练，提升TFM跨域泛化，F1提升14%。
AI 中文摘要
时间基础模型（TFM）旨在跨领域、数据集和任务进行泛化。然而，其发展仍受限于碎片化、任务特定的数据格式、标注和处理流程。我们提出了TimeNet，一个开源的数据标准和可扩展基础设施，它将时间数据与任务定义解耦，并在一个共享、可扩展的数据模型中表示信号、元数据、标注和监督信息。TimeNet支持具有规则、不规则或有序时间轴的多模态信号，并将不同的任务族（包括分类、预测、时间定位、问答、生成和编辑）表达为对同一记录的可重用视图。这种共享表示使得异构时间序列数据集能够被组合，用于跨领域、模态和任务的大规模模型训练。我们通过转码包含150万任务实例的数据集来展示TimeNet，这些实例涵盖多样化的领域、模态、时间尺度和监督形式，同时相对于原生格式保持了实用的I/O性能。TimeNet使得现有的TFN训练流程能够仅通过配置更改，即可支持在可配置数量的异构数据集上进行联合训练。我们通过跨多个数据集和任务训练TFM来展示这一能力，与在单个数据集上训练的模型相比，获得了14%的F1分数提升。这些结果表明，TimeNet提供了所需的数据和系统基础，以超越任务和数据集特定的TFM，迈向能够从共同数据模型中跨异构领域、模态、时间尺度和监督形式进行联合学习的模型。
英文摘要
Temporal Foundation Models (TFMs) aim to generalize across domains, datasets, and tasks. Yet, their development remains constrained by fragmented, task-specific data formats, annotations, and processing pipelines. We introduce TimeNet, an open-source data standard and scalable infrastructure that decouples temporal data from task definitions and represents signals, metadata, annotations, and supervision in a shared, extensible data model. TimeNet supports multimodal signals with regular, irregular, or ordinal time axes and expresses different task families (including classification, forecasting, temporal localization, question answering, generation, and editing) as reusable views over the same recordings. This shared representation enables heterogeneous time-series datasets to be combined for large-scale model training across domains, modalities, and tasks. We demonstrate TimeNet by transcoding datasets with 1.5M task instances spanning diverse domains, modalities, temporal scales, and forms of supervision, while retaining practical I/O performance relative to native formats. TimeNet enables an existing TFN training pipeline to support joint training on a configurable number of heterogeneous datasets through configuration changes alone. We show this capability by training TFM across multiple datasets and tasks, obtaining a 14% F1 score improvement compared with models trained on individual datasets. These results show that TimeNet provides the data and systems foundation needed to move beyond task- and dataset-specific TFMs toward models that can learn jointly across heterogeneous domains, modalities, temporal scales, and forms of supervision from a common data model.
