Time Weaver: A Conditional Time Series Generation Model

Time Weaver is an innovative diffusion-based model which takes into account heterogeneous metadata to advance the field of time series generation. This approach addresses the challenge of metadata heterogeneity in generating realistic time series data.

• Time Weaver incorporates variables like categorical, continuous, and even time-variant data.
• The model showcases significant improvements in generating time series over benchmarks such as GANs.
• A novel evaluation metric is proposed to assess conditional generation approaches more accurately.
• Time Weaver’s performance was validated using real-world datasets from sectors like energy, medical, air quality, and traffic.

The development of Time Weaver is a breakthrough for those who depend on accurate time series forecasting in various domains. It also sets a new standard for evaluating conditional generative models, marking a step forward in generating high-fidelity time series data.