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<p>Although Transformers perform well in time series prediction, they struggle when dealing with real-world data where the joint distribution changes over time. Previous studies have focused on reducing the non-stationarity of sequences through smoothing, but this approach strips the sequences of their inherent non-stationarity, which may lack predictive guidance for sudden events in the real world. To address the contradiction between sequence predictability and model capability, this paper proposes an efficient model design for multivariate non-stationary time series based on Transformers. This design is based on two core components: (1)Low-cost non-stationary attention mechanism, which restores intrinsic non-stationary information to time-dependent relationships at a lower computational cost by approximating the distinguishable attention learned in the original sequence.; (2) dual-data-stream Progressively learning, which designs an auxiliary output stream to improve information aggregation mechanisms, enabling the model to learn residuals of supervised signals layer by layer.The proposed model outperforms the mainstream Tranformer with an average improvement of 5.3% on multiple datasets, which provides theoretical support for the analysis of non-stationary engineering data.</p>

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NSPLformer: exploration of non-stationary progressively learning model for time series prediction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月7日

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  • Sun Jiaxing,
  • Li Yanhui,
  • Zhao Yuying

摘要

Although Transformers perform well in time series prediction, they struggle when dealing with real-world data where the joint distribution changes over time. Previous studies have focused on reducing the non-stationarity of sequences through smoothing, but this approach strips the sequences of their inherent non-stationarity, which may lack predictive guidance for sudden events in the real world. To address the contradiction between sequence predictability and model capability, this paper proposes an efficient model design for multivariate non-stationary time series based on Transformers. This design is based on two core components: (1)Low-cost non-stationary attention mechanism, which restores intrinsic non-stationary information to time-dependent relationships at a lower computational cost by approximating the distinguishable attention learned in the original sequence.; (2) dual-data-stream Progressively learning, which designs an auxiliary output stream to improve information aggregation mechanisms, enabling the model to learn residuals of supervised signals layer by layer.The proposed model outperforms the mainstream Tranformer with an average improvement of 5.3% on multiple datasets, which provides theoretical support for the analysis of non-stationary engineering data.