Multivariate time series forecasting is essential in domains such as finance, transportation, climate, and energy. However, existing patch-based methods typically adopt fixed-length segmentation, overlooking the heterogeneity of local temporal dynamics and the decoding heterogeneity of forecasting. Such designs lose details in information-dense regions, introduce redundancy in stable segments, and fail to capture the distinct complexities of short-term and long-term horizons. We propose TimeMosaic, a forecasting framework that aims to address temporal heterogeneity. TimeMosaic employs adaptive patch embedding to dynamically adjust granularity according to local information density, balancing motif reuse with structural clarity while preserving temporal continuity. In addition, it introduces segment-wise decoding that treats each prediction horizon as a related subtask and adapts to horizon-specific difficulty and information requirements, rather than applying a single uniform decoder. Extensive evaluations on benchmark datasets demonstrate that TimeMosaic delivers consistent improvements over existing methods, and our model trained on the large-scale corpus with 321 billion observations achieves performance competitive with state-of-the-art TSFMs.

翻译：多元时间序列预测在金融、交通、气候和能源等领域具有关键意义。然而，现有基于片段的方法通常采用固定长度分割，忽略了局部时序动态的异质性以及预测任务的解码异质性。此类设计会在信息密集区域丢失细节，在平稳片段中引入冗余，且无法捕捉短期与长期预测区间截然不同的复杂性。本文提出TimeMosaic预测框架，旨在应对时序异质性挑战。TimeMosaic采用自适应片段嵌入机制，根据局部信息密度动态调整粒度，在保持时序连续性的同时平衡模式复用与结构清晰度。此外，该框架引入分段解码机制，将每个预测区间视为相关子任务，并适应不同预测区间的特定难度与信息需求，而非使用单一的统一解码器。在基准数据集上的大量实验表明，TimeMosaic相较现有方法实现了持续的性能提升，且基于包含3210亿观测值的大规模语料库训练的模型达到了与当前最先进时间序列基础模型相竞争的性能水平。