Forecasting Irregular Multivariate Time Series (IMTS) has recently emerged as a distinct research field, necessitating specialized models to address its unique challenges. While most forecasting literature assumes regularly spaced observations without missing values, many real-world datasets - particularly in healthcare, climate research, and biomechanics - violate these assumptions. Time Series (TS)-mixer models have achieved remarkable success in regular multivariate time series forecasting. However, they remain unexplored for IMTS due to their requirement for complete and evenly spaced observations. To bridge this gap, we introduce IMTS-Mixer, a novel forecasting architecture designed specifically for IMTS. Our approach retains the core principles of TS mixer models while introducing innovative methods to transform IMTS into fixed-size matrix representations, enabling their seamless integration with mixer modules. We evaluate IMTS-Mixer on a benchmark of four real-world datasets from various domains. Our results demonstrate that IMTS-Mixer establishes a new state-of-the-art in forecasting accuracy while also improving computational efficiency.

翻译：不规则多元时间序列预测近年来已发展为一个独立的研究领域，需要专门模型以应对其独特挑战。尽管多数预测文献假设观测值等间距且无缺失，但许多现实世界数据集——尤其是在医疗健康、气候研究和生物力学领域——违背了这些假设。时间序列混合模型在规则多元时间序列预测中取得了显著成功，但由于其要求完整且均匀间隔的观测值，尚未被应用于不规则多元时间序列。为填补这一空白，我们提出了IMTS-Mixer，一种专为不规则多元时间序列设计的新型预测架构。该方法保留了时间序列混合模型的核心原理，同时引入了创新技术，将不规则多元时间序列转换为固定大小的矩阵表示，从而实现其与混合模块的无缝集成。我们在来自不同领域的四个真实世界数据集基准上评估了IMTS-Mixer。结果表明，IMTS-Mixer在预测精度上确立了新的最优水平，同时提升了计算效率。