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Recent Advances in Time Series Forecasting Methods
DOI:10.3390/app16031417.png)
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A
IF:
2.5
论文数:
7.6K
被引数:
4
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引用论文
Kummaraka, U.; Srisuradetchai, P. Monte Carlo Dropout Neural Networks for Forecasting Sinusoidal Time Series: Performance Evaluation and Uncertainty Quantification. Appl. Sci. 2025, 15, 4363. [Google Scholar] [CrossRef]Kummaraka, U.; Srisuradetchai, P. 蒙特卡洛 dropout 神经网络在预测正弦时间序列中的应用:性能评估与不确定性量化。Appl. Sci. 2025, 15, 4363. [Google Scholar] [CrossRef]
Bibliometric Insights into Time Series Forecasting and AI Research: Growth, Impact, and Future DirectionsDomenteanu, A.; Diaconu, P.; Delcea, C. 时间序列预测与AI研究:计量学洞察——增长、影响及未来方向. Appl. Sci. 2025, 15, 6221. [Google Scholar] [CrossRef]
A Novel Hybrid Framework for Stock Price Prediction Integrating Adaptive Signal Decomposition and Multi-Scale Feature Extraction一种集成自适应信号分解和多尺度特征提取的股票价格预测新型混合框架
Enhancing Demand Forecasting Using the Formicary Zebra Optimization with Distributed Attention Guided Deep Learning Model利用蚁群斑马优化算法与分布式注意力引导深度学习模型提升需求预测
MMHFormer: Multi-Source and Multi-View Hierarchical Transformer for Traffic Flow PredictionMMHFormer:多源和多视图分层Transformer用于交通流量预测
Hybrid ML/DL Approach to Optimize Mid-Term Electrical Load Forecasting for Smart Buildings混合ML/DL方法优化智能建筑中期电力负荷预测
TFHA: A Time-Frequency Harmonic Attention Framework for Analyzing Digital Management Strategy Impact MechanismsTFHA:一种用于分析数字管理策略影响机制的时频谐波注意力框架

