[1]郝剑龙,杜银焕,薛令荣,等.一种复值多域损失融合的时间序列预测网络[J].智能系统学报,2026,21(5):1211-1220.[doi:10.11992/tis.202511025]
HAO Jianlong,DU Yinhuan,XUE Lingrong,et al.A complex-valued multi-domain loss fusion network for time series forecasting[J].CAAI transactions on intelligent systems,2026,21(5):1211-1220.[doi:10.11992/tis.202511025]
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HAO Jianlong,DU Yinhuan,XUE Lingrong,et al.A complex-valued multi-domain loss fusion network for time series forecasting[J].CAAI transactions on intelligent systems,2026,21(5):1211-1220.[doi:10.11992/tis.202511025]
一种复值多域损失融合的时间序列预测网络
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第5期
页码:
1211-1220
栏目:
学术论文—机器学习
出版日期:
2026-09-05
- Title:
- A complex-valued multi-domain loss fusion network for time series forecasting
- Keywords:
- time series forecasting; complex-valued neural networks; loss function; complex attention mechanism; fast Fourier transform; phase aware; time-frequency fusion; dynamic gradient balancing
- 分类号:
- TP301.6
- 摘要:
- 时间序列预测在电力、气象、交通等关键领域具有广泛的应用价值，传统预测模型受限于损失函数单一、实数域相位信息丢失及固定权重优化失衡，难以适配复杂时序特性。本文提出了一种基于复值多域损失融合的时间序列预测网络(complex-valued multi-domain loss fusion time series forecasting network, CFuseTS)，利用复值卷积与共轭复值注意力方法联合捕捉时序数据的幅度与相位特征，突破实数域建模的局限；同时构建多域协同损失模块对多元时间序列进行联合约束，依托动态梯度均衡算法实时调整各域权重，解决传统固定权重导致的优化偏差。在 8 个基准数据集上的实验表明，CFuseTS 与现有先进方法相比，在所有预测长度(96/192/336/720 时间步)上的均方误差平均降低了 6.51%，最多降低了14.78%，充分验证了其有效性与泛化能力。
- Abstract:
- Time series forecasting possesses extensive application value in critical fields such as power systems, meteorology, and transportation. Traditional models are constrained by single temporal domain loss functions, phase information loss in the real number domain, and fixed-weight optimization imbalance, making it difficult to adapt to complex temporal characteristics. This paper proposes a complex-valued multi-domain loss fusion time series prediction network (CFuseTS). It adopts complex-valued convolution and conjugate complex-valued attention to jointly capture the amplitude and phase features of time series data, breaking the inherent limitations of real-valued modeling. Meanwhile, a multi-domain synergistic loss (MDSL) module is constructed to implement joint constraints on multivariate time series, and the dynamic gradient equalizer (DGE) dynamically adjusts the weights of each domain in real time to mitigate optimization bias from conventional fixed-weight strategies. Experiments on 8 benchmark datasets show that CFuseTS reduces mean squared error (MSE) by an average of 6.51% and a maximum of 14.78% for all predicted lengths (96/192/336/720 time steps) compared to existing advanced methods, fully validating its effectiveness and generalization ability.
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备注/Memo
收稿日期:2025-11-19。
基金项目:山西省基础研究计划自然科学研究面上项目(202303021221185).
作者简介:郝剑龙，副教授，博士，主要研究方向为深度学习、时间序列分析。主持国家自然科学基金青年项目1项，参与省部级课题多项。E-mail：haojianlong2012@sxufe.edu.cn。;杜银焕，硕士研究生，主要研究方向为时间序列分析、人工智能。E-mail：duyinhuan2024@163.com。;薛令荣，副教授，博士，主要研究方向为随机非线性系统控制。主持国家自然科学基金青年项目1项、省部级课题2项，发表学术论文20余篇。E-mail：20181058@sxufe.edu.cn。
通讯作者:郝剑龙. E-mail：haojianlong2012@sxufe.edu.cn
更新日期/Last Update:
2026-09-05