TimeNet: Open-source data infrastructure for Temporal Foundation Models (github.com)

TimeNet has been announced as an innovative open-source data infrastructure designed specifically for Temporal Foundation Models, simplifying the challenges associated with handling time-series datasets. By introducing the TimeF format, TimeNet unifies various incompatible storage layouts into one shared format. This allows users to find, build, download, and load time-series datasets through a single API, while keeping model training, inference, and evaluation within their preferred frameworks. The launch is particularly significant for the AI/ML community, as it streamlines the process of working with time-series data, which is crucial for applications ranging from forecasting to classification. The ongoing TimeNet Online Hackathon invites participants to solve time-series problems by onboarding datasets in the TimeNet format and training temporal AI models, such as OpenTSLM. Key technical components include the creation of a registry for storing immutable TimeF versions and the use of connectors to convert raw data sources into the TimeF format, which encapsulates essential metadata and signal values. The tools are designed for ease of use with Python, making it accessible for a broad range of developers and researchers interested in advancing temporal AI capabilities. Overall, TimeNet aims to facilitate rapid experimentation and innovation in the field of time-series analysis and forecasting.