用卷积模块实现长时序预测，自动捕捉趋势与波动。
ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting
- 设计双卷积结构，分别处理趋势与局部变化。
- 在9个数据集上表现优异，尤其擅长强趋势数据。
- 自带位置信息，可替代序列模型中的位置嵌入。
本文提出一种简单而有效的卷积模块，用于长时序预测。该模块受自回归积分滑动平均（ARIMA）模型启发，包含两个卷积组件：一个用于捕捉趋势（自回归），另一个用于细化局部波动（移动平均）。与传统ARIMA需迭代多步预测不同，该模块可直接进行多步预测，便于扩展至多变量场景。在九个常用基准数据集上的实验表明，所提方法在具有显著趋势变化的数据上表现突出，同时保持架构简洁。进一步分析显示，该模块天然编码绝对位置信息，具备作为序列模型轻量级位置嵌入的潜力。
原文摘要 · Abstract (English)
This paper proposes a simple yet effective convolutional module for long-term time series forecasting. The proposed block, inspired by the Auto-Regressive Integrated Moving Average (ARIMA) model, consists of two convolutional components: one for capturing the trend (autoregression) and the other for refining local variations (moving average). Unlike conventional ARIMA, which requires iterative multi-step forecasting, the block directly performs multi-step forecasting, making it easily extendable to multivariate settings. Experiments on nine widely used benchmark datasets demonstrate that our method ARMA achieves competitive accuracy, particularly on datasets exhibiting strong trend variations, while maintaining architectural simplicity. Furthermore, analysis shows that the block inherently encodes absolute positional information, suggesting its potential as a lightweight replacement for positional embeddings in sequential models.
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