NSTL国家科技图书文献中心

1. Revisiting the Efficacy of Signal Decomposition in AI-based Time Series Prediction NSTL国家科技图书文献中心

Dan Zhang | Kexin Jiang ... - 《The International Conference Optoelectronic Information and Optical Engineering (OIOE2024),Part One of Two Parts》 - International Conference Optoelectronic Information and Optical Engineering - 2025, - 135130M.1～135130M.10 - 共10页

摘要：Time series prediction is a fundamental | paradigm in AI-driven time* series prediction is injecting* | signal decomposition in AI-based time series prediction | error in time* series modeling, and the de facto* | problem in scientific exploration and artificial

关键词： Signal processing | Time series prediction | Label leakage | Feature engineering | Re-investigation

2. Short-term time series prediction based on evolutionary interpolation of Chebyshev polynomials with internal smoothing NSTL国家科技图书文献中心

Loreta,Saunoriene | Jinde,Cao ... - 《Soft computing》 - 2025,29(1) - 375～389 - 共15页

摘要：Abstract A novel short-term time series | values of the time* series. The proposed nonlinear* | spaced time* series enables a more accurate* | time series. Computational experiments with several | standard time series* are used to demonstrate the efficacy*

关键词： Time series prediction | Internal smoothing | Chebyshev polynomials

3. Cooperative co-evolution deep echo state network for time series prediction NSTL国家科技图书文献中心

Chen, Jianwei | Lun, Shuxian ... - 《Physica Scripta》 - 2025,100(2) - 共19页

摘要： strong predictive performance in time* series tasks, but* | CCEDESN model achieves superior prediction* accuracy* | The deep echo state network demonstrates | inter-layer weight optimization and reservoir | structure design are still challenging tasks. To address

关键词： echo state networks | hierarchical reservoir computing | time series prediction | cooperative co-evolution | modularization

4. Sparse compressed deep echo state network with improved arithmetic optimization algorithm for chaotic time series prediction NSTL国家科技图书文献中心

Hongbo Wang | Yuanbin Mo - 《Expert Systems with Application》 - 2025,259(Jan.) - 125249.1～125249.14 - 共14页

摘要： chaotic time* series. The outcomes of the simulation* | efficiency while maintaining prediction* accuracy. This* | A sparse compressed deep echo state network | (SCDESN) incorporating sparse input units, compressed | sampling and principal component analysis (PCA) units is

关键词： Echo state networks | Hierarchical structure | Deep learning | Arithmetic optimization algorithm | Chaotic time series prediction

5. MR-Transformer: Multiresolution Transformer for Multivariate Time Series Prediction NSTL国家科技图书文献中心

Siying Zhu | Jiawei Zheng ... - 《IEEE transactions on neural networks and learning systems》 - 2025,36(1) - 1171～1183 - 共13页

摘要：Multivariate time* series (MTS) prediction has* | . Extensive experiments conducted on real-world time series | transformer (MR-Transformer) for MTS prediction, modeling | multiresolution features between both time* steps and variables* | outperforms the state-of-the-art MTS prediction* models. The*

关键词： Time series analysis | Transformers | Forecasting | Predictive models | Feature extraction | Adaptation models | Convolution

6. An Adaptive Continual Learning Method for Nonstationary Industrial Time Series Prediction NSTL国家科技图书文献中心

Mengqing Wu | Xiaofeng Zhou ... - 《IEEE transactions on industrial informatics》 - 2025,21(2) - 1160～1169 - 共10页

摘要： nonstationary industrial time* series prediction. Our approach | accuracy and efficiency of industrial time series* | prediction. However, the dynamic changes in industrial | over time* and fails to adapt to new operating* | operating condition. Lastly, a time-sensitive activation

关键词： Continuing education | Adaptation models | Predictive models | Data models | Training | Time series analysis | Computational modeling | Production | Optimization | Service robots

7. A Transformer-Based Industrial Time Series Prediction Model With Multivariate Dynamic Embedding NSTL国家科技图书文献中心

Chenze Wang | Han Wang ... - 《IEEE transactions on industrial informatics》 - 2025,21(2) - 1813～1822 - 共10页

摘要：Industrial time* series prediction (ITSP) is* | industrial time* series, raising the difficulty of* | dynamic distribution features of time* series, and the* | industry. However, time-varying conditions and complex | prediction. This article proposes an ITSP model considering

关键词： Time series analysis | Predictive models | Vectors | Training | Informatics | Feature extraction | Transformers | Resource management | Heuristic algorithms | Concept drift

8. A novel approach of multi-channel attention mechanism for long-sequential multivariate time-series prediction problem NSTL国家科技图书文献中心

Tham,Vo | Linh Nguyen Thi,My - 《Soft computing》 - 2025,29(2) - 629～644 - 共16页

摘要： sequential time-series* is always considered as a* | intricate time-series* datasets. In recent years* | attention transformer for time-series* (MAT4TS). Our MAT4TS* | -world long-sequential time-series* datasets* | -series* prediction problem in comparing with previous*

关键词： Multivariate time-series | Transformer | Multi-channel attention

9. Deep Learning Network Based Time Series Prediction Model for Cyanobacterial Concentration Using a Many-Objective Algorithm NSTL国家科技图书文献中心

Bao Liu | Jiaxin Li - 《Intelligent Robotics and Applications,Part III》 - International Conference on Intelligent Robotics and Applications - 2025, - 325～335 - 共11页

摘要： paper proposes a time* series prediction model for* | worldwide. Therefore, accurate prediction* of blue-green* | effective algae management. However, current prediction | enhance cyanobacterial bloom prediction* accuracy, this* | prediction. Lastly, the predicted outputs are combined to

关键词： Discrete wavelet transform | Gated recurrent unit | Many-objective algorithm

10. A Correntropy-Based Echo State Network with Application to Time Series Prediction NSTL国家科技图书文献中心

Xiufang Chen | Zhenming Su ... - 《IEEE/CAA journal of automatica sinica》 - 2025,12(2) - 425～435 - 共11页

摘要：As a category of recurrent neural networks | , echo state networks (ESNs) have been the topic of in | -depth investigations and extensive applications in a | diverse array of fields, with spectacular triumphs | achieved. Nevertheless, the traditional ESN and the

关键词： Training | Incremental learning | Time series analysis | Noise | Echo state networks | Benchmark testing | Prediction algorithms | Robustness | Noise measurement | Optimization

摘要：Time series prediction is a fundamental | paradigm in AI-driven time series prediction is injecting | signal decomposition in AI-based time series prediction | error in time series modeling, and the de facto | problem in scientific exploration and artificial

关键词： Signal processing | Time series prediction | Label leakage | Feature engineering | Re-investigation

摘要：Abstract A novel short-term time series | values of the time series. The proposed nonlinear | spaced time series enables a more accurate | time series. Computational experiments with several | standard time series are used to demonstrate the efficacy

关键词： Time series prediction | Internal smoothing | Chebyshev polynomials

摘要： strong predictive performance in time series tasks, but | CCEDESN model achieves superior prediction accuracy | The deep echo state network demonstrates | inter-layer weight optimization and reservoir | structure design are still challenging tasks. To address