  Imagine generating a city's electricity demand pattern based on weather, the
presence of an electric vehicle, and location, which could be used for capacity
planning during a winter freeze. Such real-world time series are often enriched
with paired heterogeneous contextual metadata (weather, location, etc.).
Current approaches to time series generation often ignore this paired metadata,
and its heterogeneity poses several practical challenges in adapting existing
conditional generation approaches from the image, audio, and video domains to
the time series domain. To address this gap, we introduce Time Weaver, a novel
diffusion-based model that leverages the heterogeneous metadata in the form of
categorical, continuous, and even time-variant variables to significantly
improve time series generation. Additionally, we show that naive extensions of
standard evaluation metrics from the image to the time series domain are
insufficient. These metrics do not penalize conditional generation approaches
for their poor specificity in reproducing the metadata-specific features in the
generated time series. Thus, we innovate a novel evaluation metric that
accurately captures the specificity of conditional generation and the realism
of the generated time series. We show that Time Weaver outperforms
state-of-the-art benchmarks, such as Generative Adversarial Networks (GANs), by
up to 27% in downstream classification tasks on real-world energy, medical, air
quality, and traffic data sets.
