IConv：通过通道独立卷积聚焦局部变化用于多元时间序列预测
IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting
AI总结：
针对多元时间序列预测中MLP忽略局部变化的问题，提出IConv，结合CNN与MLP，通过通道独立卷积建模局部模式，实验证明其优越性。
摘要：
现实世界的时间序列数据通常表现出非平稳性，包括变化的趋势、不规则的季节性和残差。在变化趋势方面，最近提出的基于多层感知器（MLP）的模型因其计算效率和捕获长期依赖的能力而表现出优异的性能。然而，MLP架构的线性特性在应用于具有不同分布的通道时存在局限性，导致诸如季节性模式和残差分量等局部变化被忽略。相比之下，卷积神经网络（CNN）可以有效地整合这些变化。为了解决MLP的局限性，我们提出将其与CNN相结合。整体趋势使用MLP建模以考虑长期依赖。CNN使用多种卷积核与MLP趋势预测相结合，对细粒度的局部模式进行建模。为了专注于局部变化的建模，我们提出了IConv，一种新颖的卷积架构，它独立处理时间依赖通道，并通过不同的层考虑通道间关系。独立的通道处理能够对多样的局部时间依赖进行建模，并采用较大的卷积核。不同的通道间考虑方式降低了计算成本。所提出的模型通过在时间序列数据集上的大量实验进行评估。结果表明，所提出的方法在多元时间序列预测中具有优越性。
英文摘要：
Real-world time-series data often exhibit non-stationarity, including changing trends, irregular seasonality, and residuals. In terms of changing trends, recently proposed multi-layer perceptron (MLP)-based models have shown excellent performance owing to their computational efficiency and ability to capture long-term dependency. However, the linear nature of MLP architectures poses limitations when applied to channels with diverse distributions, resulting in local variations such as seasonal patterns and residual components being ignored. However, convolutional neural networks (CNNs) can effectively incorporate these variations. To resolve the limitations of MLP, we propose combining them with CNNs. The overall trend is modeled using an MLP to consider long-term dependencies. The CNN uses diverse kernels to model fine-grained local patterns in conjunction with MLP trend predictions. To focus on modeling local variation, we propose IConv, a novel convolutional architecture that processes the temporal dependency channel independently and considers the inter-channel relationship through distinct layers. Independent channel processing enables the modeling of diverse local temporal dependencies and the adoption of a large kernel size. Distinct inter-channel considerations reduce computational cost. The proposed model is evaluated through extensive experiments on time-series datasets. The results reveal the superiority of the proposed method for multivariate time-series forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
