Aug 2026· 2026 IEEE International Conference on Mechatronics and Automation (ICMA)· pp. 353-358· 0 citations· 21 references
Abstract
Time series of dynamic system resources typically exhibit complex long- and short-term dependencies, posing significant challenges for accurate multi-step forecasting. To address the limitations of conventional single-architecture models in capturing both global patterns and local dynamics, this paper proposes an enhanced multivariate forecasting model, PatchTST_SLG(Serial LSTM-GRU). This model utilizes the Patch Time Series Transformer (PatchTST) as the backbone to extract global dependencies from long sequences and serially integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) modules at the output to smooth prediction trajectories and correct local errors. Experiments on the Abilene dataset demonstrate that the proposed model significantly outperforms baseline models, including LSTM, GRU, and the original PatchTST, achieving substantial reductions in MAE and RMSE. The results confirm the effectiveness of this serial structure and its significant contribution to improving predictive stability.
Experimental results show that the proposed model outperforms traditional RNN and LSTM models across various tasks, indicating promising potential for practical applications.
Ze Zhao, Ming-Yan Jiang, Feng Wang· International Conference on...· 0 citations
The Decomposed Recurrent Neural Network (DeRNN) is proposed, which decouples global trend modeling from local fluctuation extraction via an asymmetric dual-track architecture and exhibits superior robustness against noise and distribution shifts.
Shanyun Qian· Poster Volume 0008 The 2026...· 0 citations
Precise short-term electric load prediction plays a vital role in power system analysis, scheduling, generation planning and secure grid operation. Nevertheless, load sequences typically show nonlinear fluctuations, time-dependent correlations and noise interference, which bring challenges to direct forecasting tasks....
Xing Yu· Sixth International Conferen...· 0 citations
This review compares three representative Transformer-based models and indicates that model complexity is not the sole determinant of forecasting performance and that look-back windows and experimental protocols also affect model comparisons.
Xu-Hui Ren· Applied and Computational En...· 0 citations
The findings indicate that passing attention-derived context into a bidirectional memory module offers a practical means of combining long-horizon structure with local temporal variation, although computational cost remains relevant for latency-sensitive trading applications.
The findings indicate that the proposed architecture successfully reconciles multi-scale feature extraction with lightweight dependency modeling, enhancing structural generalization and providing a scalable framework for real-time temporal analysis in complex industrial environments.
