History-Guided Slice Self-Attention for Mitigating Forecast Smoothing in Irregular Time Series

Irregular multivariate time series (IMTS) often provide sparse, asynchronous observations, leaving fine-scale future dynamics weakly constrained. Although diffusion models can generate fine-scale temporal structure, mixing across distinct dynamics in existing IMTS forecasting architectures can suppress temporal variation, leading to forecast smoothing with high-frequency attenuation. Grouping based on noisy query states can amplify such dynamics mixing, motivating history-guided grouping that maintains distinct slice representations and preserves structured variation. To address this, we propose Diffusion Coordinate Transformer with history-guided Slice self-attention (DiCTS) for joint probabilistic IMTS forecasting. Its denoiser employs History-guided Slice Self-Attention (HiSSA), which derives query-to-slice assignments from encoded history and query coordinates rather than noisy query states, while aggregating sample-specific features from the current denoising states. This history-guided assignment prevents injected diffusion noise from influencing the grouping and distinguishes queries with distinct dynamics, preventing attenuation of structured variation. We demonstrate DiCTS with established IMTS benchmarks, indicating competitive probabilistic forecasting performance and point-prediction accuracy. Qualitative and spectral analyses further show that DiCTS mitigates forecast smoothing and more faithfully follows future trajectories.