SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement

• URL: http://arxiv.org/abs/2602.00589v1
• Date: Sat, 31 Jan 2026 08:12:24 GMT
• Title: SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacement
• Authors: Xiangfei Qiu, Xvyuan Liu, Tianen Shen, Xingjian Wu, Hanyin Cheng, Bin Yang, Jilin Hu,
• Abstract summary: This study proposes a robust time series forecasting framework called SEER.<n>In real-world time series, there are often low-quality issues during data collection, such as missing values, distribution shifts, anomalies and white noise.<n>We introduce a Learnable Patch Replacement Module, which enhances forecasting robustness and model accuracy through a two-stage process.
• Score: 9.482558107303646
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Time series forecasting is important in many fields that require accurate predictions for decision-making. Patching techniques, commonly used and effective in time series modeling, help capture temporal dependencies by dividing the data into patches. However, existing patch-based methods fail to dynamically select patches and typically use all patches during the prediction process. In real-world time series, there are often low-quality issues during data collection, such as missing values, distribution shifts, anomalies and white noise, which may cause some patches to contain low-quality information, negatively impacting the prediction results. To address this issue, this study proposes a robust time series forecasting framework called SEER. Firstly, we propose an Augmented Embedding Module, which improves patch-wise representations using a Mixture-of-Experts (MoE) architecture and obtains series-wise token representations through a channel-adaptive perception mechanism. Secondly, we introduce a Learnable Patch Replacement Module, which enhances forecasting robustness and model accuracy through a two-stage process: 1) a dynamic filtering mechanism eliminates negative patch-wise tokens; 2) a replaced attention module substitutes the identified low-quality patches with global series-wise token, further refining their representations through a causal attention mechanism. Comprehensive experimental results demonstrate the SOTA performance of SEER.

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