DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
DiTS proposes Diffusion Transformers for Time Series, a multimodal model that treats historical targets and external covariates as separate inputs. It jointly conditions future generation on both target history and covariates to forecast time series. This approach uses time-aligned modulation to link these modalities effectively, achieving strong performance in both point and distributional forecasting.