ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2509.10324v1
• Date: Fri, 12 Sep 2025 15:03:49 GMT
• Title: ARMA Block: A CNN-Based Autoregressive and Moving Average Module for Long-Term Time Series Forecasting
• Authors: Myung Jin Kim, YeongHyeon Park, Il Dong Yun,
• Abstract summary: The proposed block is inspired by the Auto-Regressive Integrated Moving Average (ARIMA) model.<n>Unlike conventional ARIMA, which requires iterative multi-step forecasting, the block directly performs multi-step forecasting.<n>The block inherently encodes absolute positional information, suggesting its potential as a lightweight replacement for positional embeddings in sequential models.
• Score: 3.0641475192265495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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