TimeNet: An Extensible Unified Data Infrastructure for Next-Generation Temporal Foundation Models

2026-10-03

AI Summary

This study addresses the challenges of data format fragmentation and task-specific pipelines that hinder cross-domain generalization in time-series foundation model development. To this end, it proposes a scalable, unified, open-source data standard that decouples multimodal signals from task definitions. By leveraging a shared data model, heterogeneous task families are expressed as reusable views of a single record, enabling the construction of a configuration-driven training pipeline for joint training. The project efficiently transcodes 1.5 million instances, and cross-dataset joint training yields a 14% improvement in F1 score. Ultimately, this work provides a universal data infrastructure to facilitate the large-scale research and development of time-series foundation models.

Abstract

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.

Problem

• Temporal Foundation Models
• data fragmentation
• heterogeneous time-series
• cross-domain generalization
• unified data infrastructure

Innovation

• Temporal Foundation Models
• Unified Data Infrastructure
• Multimodal Time Series
• Cross-task Generalization
• Joint Training