OpenTS - Democratizing AI for Time series and Spatio-temporal data

OpenTS-Bench Pipeline

The below figure provides a visual overview of TFB's pipeline.

• The data layer is a repository of univariate and multivariate time series from diverse domains, structured according to their distinct characteristics, frequencies, and sequence lengths. The data is uniformly according to a standardized format.
• The method layer supports embedding statistical learning, machine learning and deep learning methods. Additionally, TFB is designed to be compatible with any third-party TSF library, such as Darts, TSlib. Users can easily integrate forecasting methods implemented in third-party libraries into TFB by writing a simple Universal Interface, facilitating fair comparisons.
• The evaluation layer offers support for a diverse range of evaluation strategies and metrics. And it also covers evaluation metrics found in other studies and enables the use of customized metrics for a more comprehensive assessment of method performance.
• The reporting layer encompasses a logging system for tracking information, enabling the capture of experimental settings to enable traceability.