Multivariate long-term time series forecasting aims to predict future sequences by utilizing historical observations, with a core focus on modeling intra-sequence and cross-channel dependencies. Numerous studies have developed diverse architectures to capture these patterns, achieving significant improvements in forecasting accuracy. Among them, iTransformer, a representative method for channel information extraction, leverages the Transformer architecture to model channel-wise dependencies, thereby facilitating sequence transformation for enhanced forecasting performance. Building upon iTransformer's channel extraction concept, we propose AverageTime, a simple, efficient, and scalable forecasting model. Beyond iTransformer, AverageTime retains the original sequence information and reframes channel extraction as a stackable and extensible architecture. This allows the model to generate multiple novel sequences through various structural mechanisms, rather than being limited to transforming the original input. Moreover, the newly extracted sequences are not restricted to channel processing; other techniques such as series decomposition can also be incorporated to enhance predictive accuracy. Additionally, we introduce a channel clustering technique into AverageTime, which substantially improves training and inference efficiency with negligible performance loss. Experiments on real-world datasets demonstrate that with only two straightforward averaging operations, applied to both the extracted sequences and the original series. AverageTime surpasses state-of-the-art models in forecasting performance while maintaining near-linear complexity. This work offers a new perspective on time series forecasting: enriching sequence information through extraction and fusion. The source code is available at https://github.com/ UniqueoneZ/AverageTime.

翻译：多元长期时间序列预测旨在利用历史观测值预测未来序列，其核心在于建模序列内与跨通道的依赖关系。众多研究已开发出多种架构以捕捉这些模式，在预测精度上取得了显著提升。其中，iTransformer 作为通道信息提取的代表性方法，利用 Transformer 架构建模通道间依赖关系，从而促进序列变换以提升预测性能。基于 iTransformer 的通道提取思想，我们提出了 AverageTime，一个简单、高效且可扩展的预测模型。在 iTransformer 的基础上，AverageTime 保留了原始序列信息，并将通道提取重构为可堆叠和可扩展的架构。这使得模型能够通过多种结构机制生成多个新颖序列，而非仅限于变换原始输入。此外，新提取的序列不仅限于通道处理；其他技术如序列分解也可融入其中以提升预测精度。另外，我们在 AverageTime 中引入了通道聚类技术，该技术以可忽略的性能损失大幅提升了训练和推理效率。在真实数据集上的实验表明，仅通过对提取序列和原始序列进行两次简单的平均操作，AverageTime 在预测性能上超越了现有最佳模型，同时保持了接近线性的复杂度。这项工作为时间序列预测提供了新的视角：通过提取与融合来丰富序列信息。源代码可在 https://github.com/UniqueoneZ/AverageTime 获取。