Matching Scales to Patterns: Soft-Segmented Multi-scale Experts for Irregular Multivariate Time Series Forecasting

Irregular multivariate time series (IMTS) are pervasive in real-world scenarios, characterized by uneven sampling intervals and misaligned observations across variables. Their temporal patterns can vary substantially across samples and periods in aspects such as fluctuation dynamics and changes in sampling density. Distinct aspects of these patterns can be captured at multiple scales, while their variation across samples and evolution over time may call for different scale combinations. However, existing multi-scale IMTS forecasting methods typically apply a fixed scale configuration to all samples and time periods, limiting their ability to adapt scales to varying temporal patterns. To address this limitation, we propose SoftScale, an adaptive soft-segmented multi-scale experts network for IMTS forecasting. SoftScale first employs data-dependent time cursors to construct sample-adaptive soft segments from raw IMTS. For each soft segment, a multi-scale experts network then captures temporal and inter-variable dependencies at each scale. To integrate these dependencies, an MoE router adaptively selects the most suitable scale combination for each soft segment and combines the corresponding scale-specific representations. In this way, SoftScale enables adaptive multi-scale modeling across different samples and periods, leading to more accurate IMTS forecasting. Experiments on four real-world datasets demonstrate that SoftScale outperforms existing state-of-the-art baselines. Our code is available at [https://anonymous.4open.science/r/SoftScale/](https://anonymous.4open.science/r/SoftScale/).