TS-Diffusion: Generating Highly Complex Time Series with Diffusion
  Models
Yangming Li
While current generative models have achieved promising performances in
time-series synthesis, they either make strong assumptions on the data format
(e.g., regularities) or rely on pre-processing approaches (e.g.,
interpolations) to simplify the raw data. In this work, we consider a class of
time series with three common bad properties, including sampling
irregularities, missingness, and large feature-temporal dimensions, and
introduce a general model, TS-Diffusion, to process such complex time series.
Our model consists of three parts under the framework of point process. The
first part is an encoder of the neural ordinary differential equation (ODE)
that converts time series into dense representations, with the jump technique
to capture sampling irregularities and self-attention mechanism to handle
missing values; The second component of TS-Diffusion is a diffusion model that
learns from the representation of time series. These time-series
representations can have a complex distribution because of their high
dimensions; The third part is a decoder of another ODE that generates time
series with irregularities and missing values given their representations. We
have conducted extensive experiments on multiple time-series datasets,
demonstrating that TS-Diffusion achieves excellent results on both conventional
and complex time series and significantly outperforms previous baselines.