Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93\%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/zyj-111/GCformer.

翻译：基于Transformer的模型已成为时间序列预测领域的有力工具。然而，这些模型在处理长输入时间序列时难以做出准确预测。一方面，它们无法捕捉时间序列数据中的全局依赖关系；另一方面，长输入序列通常导致模型规模庞大且时间复杂度较高。为解决这些局限，我们提出GCformer，该模型结合了用于处理长输入序列的结构化全局卷积分支与用于捕捉短期近期信号的局部Transformer分支。我们引入了一种统一的框架用于全局卷积核，采用了三种不同的参数化方法。全局分支中选定的结构化卷积核具有次线性复杂度，从而能够高效处理长且含噪的输入信号。在六个基准数据集上的实证研究表明，GCformer优于现有最先进方法，在多变量时间序列基准测试中均方误差降低4.38%，模型参数减少61.92%。值得注意的是，全局卷积分支可作为即插即用模块提升其他模型性能，平均提升幅度达31.93%，涵盖多种近期发布的基于Transformer的模型。我们的代码公开于https://github.com/zyj-111/GCformer。