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STDNet: A Spatio-Temporal Decomposition Neural Network for Multivariate Time Series Forecasting
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作者 Zhuolun Jiang Zefei Ning +1 位作者 Hao Miao Li Wang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第4期1232-1247,共16页
Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of f... Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting. 展开更多
关键词 time series forecasting multivariate time series spatio-temporal decomposition
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Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function
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作者 Eustache Uwimana Yatong Zhou Minghui Zhang 《Journal of Power and Energy Engineering》 2023年第8期32-54,共23页
In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access ... In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy. 展开更多
关键词 SVM Quadratic SVM long-term Electrical Load forecasting Residual Load Demand series Historical Electric Load
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Multivariate Time Series Forecasting with Transfer Entropy Graph
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作者 Ziheng Duan Haoyan Xu +2 位作者 Yida Huang Jie Feng Yueyang Wang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2023年第1期141-149,共9页
Multivariate Time Series(MTS)forecasting is an essential problem in many fields.Accurate forecasting results can effectively help in making decisions.To date,many MTS forecasting methods have been proposed and widely ... Multivariate Time Series(MTS)forecasting is an essential problem in many fields.Accurate forecasting results can effectively help in making decisions.To date,many MTS forecasting methods have been proposed and widely applied.However,these methods assume that the predicted value of a single variable is affected by all other variables,ignoring the causal relationship among variables.To address the above issue,we propose a novel end-to-end deep learning model,termed graph neural network with neural Granger causality,namely CauGNN,in this paper.To characterize the causal information among variables,we introduce the neural Granger causality graph in our model.Each variable is regarded as a graph node,and each edge represents the casual relationship between variables.In addition,convolutional neural network filters with different perception scales are used for time series feature extraction,to generate the feature of each node.Finally,the graph neural network is adopted to tackle the forecasting problem of the graph structure generated by the MTS.Three benchmark datasets from the real world are used to evaluate the proposed CauGNN,and comprehensive experiments show that the proposed method achieves state-of-the-art results in the MTS forecasting task. 展开更多
关键词 multivariate Time series(MTS)forecasting neural Granger causality graph Transfer Entropy(TE)
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Medium-Term Electric Load Forecasting Using Multivariable Linear and Non-Linear Regression 被引量:2
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作者 Nazih Abu-Shikhah Fawwaz Elkarmi Osama M. Aloquili 《Smart Grid and Renewable Energy》 2011年第2期126-135,共10页
Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to one year ahead. It suits outage and maintenance planning, as well as load switching operation. We propose ... Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to one year ahead. It suits outage and maintenance planning, as well as load switching operation. We propose a new methodol-ogy that uses hourly daily loads to predict the next year hourly loads, and hence predict the peak loads expected to be reached in the next coming year. The technique is based on implementing multivariable regression on previous year's hourly loads. Three regression models are investigated in this research: the linear, the polynomial, and the exponential power. The proposed models are applied to real loads of the Jordanian power system. Results obtained using the pro-posed methods showed that their performance is close and they outperform results obtained using the widely used ex-ponential regression technique. Moreover, peak load prediction has about 90% accuracy using the proposed method-ology. The methods are generic and simple and can be implemented to hourly loads of any power system. No extra in-formation other than the hourly loads is required. 展开更多
关键词 Medium-Term LOAD forecasting Electrical PEAK LOAD multivariABLE Regression And TIME series
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A robust autoregressive long-term spatiotemporal forecasting framework for surrogate-based turbulent combustion modeling via deep learning 被引量:1
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作者 Sipei Wua Haiou Wang Kai Hong Luo 《Energy and AI》 EI 2024年第1期300-311,共12页
This paper systematically develops a high-fidelity turbulent combustion surrogate model using deep learning.We construct a surrogate model to simulate the turbulent combustion process in real time,based on a state-oft... This paper systematically develops a high-fidelity turbulent combustion surrogate model using deep learning.We construct a surrogate model to simulate the turbulent combustion process in real time,based on a state-ofthe-art spatiotemporal forecasting neural network.To address the issue of shifted distribution in autoregressive long-term prediction,two training techniques are proposed:unrolled training and injecting noise training.These techniques significantly improve the stability and robustness of the model.Two datasets of turbulent combustion in a combustor with cavity and a vitiated co-flow burner(Cabra burner)have been generated for model validation.The effects of model architecture,unrolled time,noise amplitude,and training dataset size on the long-term predictive performance are explored.The well-trained model can be applicable to new cases by extrapolation and give spatially and temporally consistent results in long-term predictions for turbulent reacting flows that are highly unsteady. 展开更多
关键词 Turbulent combustion Detailed reaction mechanism Transient simulation Deep neural network Spatiotemporal series prediction long-term forecast stability
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A Hybrid Neural Network-based Approach for Forecasting Water Demand
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作者 Al-Batool Al-Ghamdi Souad Kamel Mashael Khayyat 《Computers, Materials & Continua》 SCIE EI 2022年第10期1365-1383,共19页
Water is a vital resource.It supports a multitude of industries,civilizations,and agriculture.However,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain i... Water is a vital resource.It supports a multitude of industries,civilizations,and agriculture.However,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain is scarce.Therefore,it is crucial to forecast water demand to provide it to sectors either on regular or emergency days.The study aims to develop an accurate model to forecast daily water demand under the impact of climatic conditions.This forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the future.Focusing on the collected data of Jeddah city,Saudi Arabia in the period between 2004 and 2018,we develop a hybrid approach that uses Artificial Neural Networks(ANN)for forecasting and Particle Swarm Optimization algorithm(PSO)for tuning ANNs’hyperparameters.Based on the Root Mean Square Error(RMSE)metric,results show that the(PSO-ANN)is an accurate model for multivariate time series forecasting.Also,the first day is the most difficult day for prediction(highest error rate),while the second day is the easiest to predict(lowest error rate).Finally,correlation analysis shows that the dew point is the most climatic factor affecting water demand. 展开更多
关键词 Water demand forecasting artificial neural network multivariate time series climatic conditions particle swarm optimization hybrid algorithm
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Computational Method for Extracting and Modeling Periodicities in Time Series
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作者 Eduardo González-Rodríguez Héctor Villalobos +1 位作者 Víctor Manuel Gomez-Munoz Alejandro Ramos-Rodríguez 《Open Journal of Statistics》 2015年第6期604-617,共14页
Periodicity is common in natural processes, however, extraction tools are typically difficult and cumbersome to use. Here we report a computational method developed in MATLAB through a function called Periods with the... Periodicity is common in natural processes, however, extraction tools are typically difficult and cumbersome to use. Here we report a computational method developed in MATLAB through a function called Periods with the aim to find the main harmonic components of time series data. This function is designed to obtain the period, amplitude and lag phase of the main harmonic components in a time series (Periods and lag phase components can be related to climate, social or economic events). It is based on methods of periodic regression with cyclic descent and includes statistical significance testing. The proposed method is very easy to use. Furthermore, it does not require full understanding of time series theory, nor require many inputs from the user. However, it is sufficiently flexible to undertake more complex tasks such as forecasting. Additionally, based on previous knowledge, specific periods can be included or excluded easily. The output results are organized into two groups. One contains the parameters of the adjusted model and their F statistics. The other consists of the harmonic parameters that best fit the original series according to their importance and the summarized statistics of the comparisons between successive models in the cyclic descent process. Periods is tested with both, simulated and actual sunspot and Multivariate ENSO Index data to show its performance and accuracy. 展开更多
关键词 Time series Cyclic Descent Harmonic PERIODICITY forecasting SUNSPOT multivariate ENSO Index
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基于注意力机制的ADE-Bi-IndRNN模型的中国粮食产量预测 被引量:1
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作者 吴彬溶 王林 《运筹与管理》 CSSCI CSCD 北大核心 2024年第1期102-107,共6页
为更加准确地预测我国粮食总产量,基于自适应差分进化算法来智能地选择基于注意力机制的双向独立循环神经网络的超参数,并考虑了粮食作物单位产量、农业生产条件、科技因素、农业保险、市场及经济因素五大类影响因素,构建了基于注意力... 为更加准确地预测我国粮食总产量,基于自适应差分进化算法来智能地选择基于注意力机制的双向独立循环神经网络的超参数,并考虑了粮食作物单位产量、农业生产条件、科技因素、农业保险、市场及经济因素五大类影响因素,构建了基于注意力机制的ADE-Bi-IndRNN粮食产量预测模型。经过预测分析得出我国2020—2024的粮食产量分别为6.67亿吨、6.72亿吨、6.80亿吨、6.99亿吨、7.02亿吨,总体呈现震荡上涨趋势,平均年增长率为1.15%。同时,通过对多个变量进行的注意力权重的分析,发现现阶段对我国粮食总产量预测贡献最大的三个变量为:谷物单位面积产量,粮食作物总播种面积,耕地灌溉面积,且政府对农业保险的政策性补贴、粮食进口量、谷物生产价格指数、农业生产资料指数也有助于提升我国的粮食总产量,并据此对我国粮食行业发展提出了建议。 展开更多
关键词 粮食产量 多因素时间序列预测 深度学习 智能算法
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VAECGAN:a generating framework for long-term prediction in multivariate time series
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作者 Xiang Yin Yanni Han +1 位作者 Zhen Xu Jie Liu 《Cybersecurity》 EI CSCD 2021年第1期337-348,共12页
Long-term prediction is still a difficult problem in data mining.People usually use various kinds of methods of Recurrent Neural Network to predict.However,with the increase of the prediction step,the accuracy of pred... Long-term prediction is still a difficult problem in data mining.People usually use various kinds of methods of Recurrent Neural Network to predict.However,with the increase of the prediction step,the accuracy of prediction decreases rapidly.In order to improve the accuracy of long-term prediction,we propose a framework Variational Auto-Encoder Conditional Generative Adversarial Network(VAECGAN).Our model is divided into three parts.The first part is the encoder net,which can encode the exogenous sequence into latent space vectors and fully save the information carried by the exogenous sequence.The second part is the generator net which is responsible for generating prediction data.In the third part,the discriminator net is used to classify and feedback,adjust data generation and improve prediction accuracy.Finally,extensive empirical studies tested with five real-world datasets(NASDAQ,SML,Energy,EEG,KDDCUP)demonstrate the effectiveness and robustness of our proposed approach. 展开更多
关键词 long-term prediction multivariate time series Attention mechanism Generating framework
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融合双注意力机制的GNN多维时间序列预测
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作者 范航舟 梅红岩 +2 位作者 赵勤 张兴 程耐 《智能系统学报》 CSCD 北大核心 2024年第5期1277-1286,共10页
针对现有多维时间序列数据(multivariate time series,MTS)预测中变量间依赖关系捕获能力不足和时间序列数据多通道信息利用不充分的问题,提出一种融合双注意力机制的多维时间序列预测模型(feature fusion and dual attention mechanism... 针对现有多维时间序列数据(multivariate time series,MTS)预测中变量间依赖关系捕获能力不足和时间序列数据多通道信息利用不充分的问题,提出一种融合双注意力机制的多维时间序列预测模型(feature fusion and dual attention mechanism based GNN,FFDA-GNN)。该模型将图神经网络与空间注意力机制融合,用于增强多变量之间依赖关系捕获能力;利用并行的多层膨胀卷积和通道注意力机制,对时间序列数据进行多通道的特征提取,实现对时间序列数据多通道信息的充分利用,从而提升预测性能。在经济、电力、交通3个领域数据集上与基准模型进行对比实验,该模型预测精度优于其他基准方法,有良好的可行性。 展开更多
关键词 多维时序预测 图神经网络 注意力机制 特征融合 时间卷积网络 深度学习 卷积神经网络 时空特征
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基于统计特征搜索的多元时间序列预测方法
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作者 潘金伟 王乙乔 +1 位作者 钟博 王晓玲 《电子与信息学报》 EI CAS CSCD 北大核心 2024年第8期3276-3284,共9页
时间序列中包含一些长期依赖关系,如长期趋势性、季节性和周期性,这些长期依赖信息的跨度可能是以月为单位的,直接应用现有方法无法显式建模时间序列的超长期依赖关系。该文提出基于统计特征搜索的预测方法来显式地建模时间序列中的长... 时间序列中包含一些长期依赖关系,如长期趋势性、季节性和周期性,这些长期依赖信息的跨度可能是以月为单位的,直接应用现有方法无法显式建模时间序列的超长期依赖关系。该文提出基于统计特征搜索的预测方法来显式地建模时间序列中的长期依赖。首先对多元时间序列中的平滑特征、方差特征和区间标准化特征等统计特征进行抽取,提高时间序列搜索对趋势性、周期性、季节性的感知。随后结合统计特征在历史序列搜索相似的序列,并利用注意力机制融合当前序列信息与历史序列信息,生成可靠的预测结果。在5个真实的数据集上的实验表明该文提出的方法优于6种最先进的方法。 展开更多
关键词 多元时间序列 预测 注意力机制 长期依赖
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基于渐进式分解架构的风电时间序列预测
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作者 丁浩 周成杰 +2 位作者 车超 赵天明 周守亮 《计算机系统应用》 2024年第7期112-120,共9页
准确预测风电机组各项指标对准确管控机组和调控电网的供需有着重要意义.预测指标任务可抽象为风电时间序列预测任务.目前时间序列预测模型主要采用深度学习模型,但是风电时间序列具有较强的波动性和随机性,导致绝大部分模型不能较好挖... 准确预测风电机组各项指标对准确管控机组和调控电网的供需有着重要意义.预测指标任务可抽象为风电时间序列预测任务.目前时间序列预测模型主要采用深度学习模型,但是风电时间序列具有较强的波动性和随机性,导致绝大部分模型不能较好挖掘风电时间序列的复杂演化特性.为解决上述问题,提出了一种基于渐进式分解架构的风电时间序列预测方法,该方法首先应用神经网络池化分解方法将复杂的依赖关系简化并应用注意力机制学习长期趋势,然后运用多变量融合捕捉模块增强了网络整体的多变量关联挖掘能力,最后,融合趋势项和周期项对风电时间序列做出准确的预测.实验结果表明,该方法在风电时间序列的多步预测中均方误差相比基线模型至高可提升24%,在多尺度预测长度下表现出预测性能稳定提升的同时,计算效率显著优于同类模型. 展开更多
关键词 多变量时间序列预测 神经网络 attention机制 时间序列分解
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隐私保护下融合联邦学习和LSTM的少数据综合能源多元负荷预测 被引量:1
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作者 陈志鹏 张勇 +2 位作者 高海荣 孙晓燕 胡荷娟 《智能系统学报》 CSCD 北大核心 2024年第3期565-574,共10页
对于用能数据不足的综合能源系统,借助相似系统的丰富数据可以为其建立高精度的多元负荷预测模型,然而,受数据安全等因素的限制,很多系统并不愿意共享自身数据。联邦学习为处理隐私保护下的少数据综合能源多元负荷预测问题提供了一个重... 对于用能数据不足的综合能源系统,借助相似系统的丰富数据可以为其建立高精度的多元负荷预测模型,然而,受数据安全等因素的限制,很多系统并不愿意共享自身数据。联邦学习为处理隐私保护下的少数据综合能源多元负荷预测问题提供了一个重要的思路,但是现有方法依然存在相似参与方识别精度不高等不足。鉴于此,本文提出一种融合联邦学习和长短期记忆网络(long short-term memory,LSTM)的少数据综合能源多元负荷预测方法(multitask learning based on shared dot product confidentiality under federated learning,MT-SDPFL)。首先,给出一种基于共享向量点积保密协议的相似参与方识别方法,用来从诸多可用的综合能源系统中选出最为相似的参与方;接着,使用参数共享联邦学习算法对选中的各参与方联合训练,结合LSTM和finetune技术建立每个参与方的多元负荷预测模型。将所提方法应用于多个实际能源系统,实验结果表明,该方法可以在数据稀疏的情况下取得高精度的多源负荷预测结果。 展开更多
关键词 多元负荷预测 综合能源系统 联邦学习 隐私保护 神经网络 少数据 时序数据预测 点积协议
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基于分解和频域特征提取的多变量长时间序列预测模型
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作者 范艺扬 张洋 +2 位作者 曾尚 曾渝 付茂栗 《计算机应用》 CSCD 北大核心 2024年第11期3442-3448,共7页
针对现有基于Transformer的多变量长时间序列预测(MLTSF)模型主要从时域中提取特征,难以直接从长时间序列分散的时间点中找出可靠依赖关系的问题,提出一种新的基于分解和频域特征提取的模型。首先,提出基于频域的周期项-趋势项的分解方... 针对现有基于Transformer的多变量长时间序列预测(MLTSF)模型主要从时域中提取特征,难以直接从长时间序列分散的时间点中找出可靠依赖关系的问题,提出一种新的基于分解和频域特征提取的模型。首先,提出基于频域的周期项-趋势项的分解方法,以降低分解过程的时间复杂度;其次,在利用周期项-趋势项分解提取序列趋势性特征的基础上,利用基于Gabor变换进行频域特征提取的Transformer网络捕捉周期性的依赖,提高预测的稳定性和鲁棒性。在5个基准数据集上的实验结果显示,与现有的先进方法相比,所提模型在MLTSF上的均方误差(MSE)平均减小了7.6%,最多减小了18.9%,有效提升了预测精度。 展开更多
关键词 多变量长时间序列预测 频域特征提取 GABOR变换 TRANSFORMER 时间序列 深度学习
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基于机器学习的电力物资需求预测
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作者 何培颖 唐昭媛 +2 位作者 傅晓菲 陈涵 陈宇 《山东电力高等专科学校学报》 2024年第2期43-47,共5页
准确、合理的电力物资需求预测,可以为物资采购打下良好的基础,为企业提前统筹资源创造有利条件。考虑历史需求数据、项目投资及施工进度,提出一种基于机器学习的需求预测模型。从外部因素、内部因素、历史数据3个方面全量梳理物资需求... 准确、合理的电力物资需求预测,可以为物资采购打下良好的基础,为企业提前统筹资源创造有利条件。考虑历史需求数据、项目投资及施工进度,提出一种基于机器学习的需求预测模型。从外部因素、内部因素、历史数据3个方面全量梳理物资需求相关的影响因子,先采用引入时间序列算法对数据进行处理,再利用多元神经网络算法构建需求预测模型。以某公司10 kV电力电缆为例进行需求预测,结果表明该模型能有效预测短期物资需求。 展开更多
关键词 物资需求预测 多元神经网络 影响因子 时间序列
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融合传递熵的图神经网络农产品期货预测模型 被引量:1
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作者 张杰 甄柳琳 +1 位作者 徐硕 翟东升 《计算机工程与应用》 CSCD 北大核心 2023年第2期321-328,共8页
针对农产品期货价格波动的非线性及国内外期货产品的联动性特征,考虑到传统神经网络预测模型未能针对多源输入变量间的因果关系进行定量表征,构建融合传递熵的图神经网络预测模型。通过计算传递熵表示节点间的邻接矩阵,作为先验信息识... 针对农产品期货价格波动的非线性及国内外期货产品的联动性特征,考虑到传统神经网络预测模型未能针对多源输入变量间的因果关系进行定量表征,构建融合传递熵的图神经网络预测模型。通过计算传递熵表示节点间的邻接矩阵,作为先验信息识别变量间的因果关系;设置多尺寸滤波器的时间卷积模块提取节点特征,用于识别序列时间依赖性;设置图卷积模块实现对节点信息及其邻域信息的传播与特征筛选,最后连接参数,输出最终的预测结果。在大豆期货数据上的实证研究表明,相较于现有的通用预测模型,该模型能够实现最佳的预测效果。 展开更多
关键词 农产品期货预测 图神经网络 传递熵 多元时间序列
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基于多尺度卷积自注意力的多维时间序列预测 被引量:1
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作者 霍纬纲 侯振环 《计算机工程与设计》 北大核心 2023年第4期1250-1258,共9页
现有的多维时间序列(mutivariate time series, MTS)预测模型大多关注序列变量间的时空依赖关系,没有考虑MTS各变量上取值的典型变化趋势,即局部上下文模式(local context pattern, LCP)。为此设计一种基于因果卷积自注意力和图卷积网络... 现有的多维时间序列(mutivariate time series, MTS)预测模型大多关注序列变量间的时空依赖关系,没有考虑MTS各变量上取值的典型变化趋势,即局部上下文模式(local context pattern, LCP)。为此设计一种基于因果卷积自注意力和图卷积网络的MTS预测模型,通过多通道多尺度因果卷积提取MTS各变量的多尺度LCP特征,采用多头自注意力机制捕获多尺度LCP间的时序依赖关系,由图卷积网络提取多尺度LCP时序特征之间的空间依赖关系。在4个公开MTS数据集上的结果表明了该预测方法预测性能的优越性。 展开更多
关键词 多维时间序列 预测 局部上下文 多通道因果卷积 图卷积网络 多头自注意力 多尺度卷积
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基于深度学习的公路运价预测模型研究 被引量:1
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作者 王敏 《物流科技》 2023年第12期24-27,共4页
随着现代物流行业的发展,公路运输作为一种重要的货物运输方式正在扮演着越来越重要的角色。公路运输具有灵活、快速、点对点等优势,尤其对于短途、小批量货物运输需求更加广泛。公路运价预测作为运输流程的关键环节之一,已成为政府部... 随着现代物流行业的发展,公路运输作为一种重要的货物运输方式正在扮演着越来越重要的角色。公路运输具有灵活、快速、点对点等优势,尤其对于短途、小批量货物运输需求更加广泛。公路运价预测作为运输流程的关键环节之一,已成为政府部门和物流业关注的焦点问题。公路运价预测任务受到多种因素影响,如市场需求、运输距离、油价和政策变化等。这些因素之间相互作用,增加了公路运价准确预测的难度。针对这个问题,文章提出了一种基于深度神经网络的公路运价预测模型,以应对公路运价预测的挑战。其主要贡献在于:传统的多变量时序预测模型采用线性或非线性回归模型,但这些模型往往受到变量之间非线性、时间动态性和复杂依赖性的建模能力限制。文章采用基于图神经网络的MTGNN模型,对公路运价预测进行了应用研究。该模型由图卷积网络和时序卷积网络两部分组成。首先,通过神经网络学习图结构并计算邻接矩阵来表示节点之间的依赖关系和实现节点聚合。然后,使用图卷积网络模块对节点信息进行传播,从而获取更准确的节点表示。接下来,时序卷积网络提取经过图卷积网络学习后结果中的时序特征,实现对未来公路运价的预测。最后,输出层对时序输出结果进行计算,得出模型预测结果。 展开更多
关键词 公路运价 多变量时序预测 图神经网络
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基于时序注意力网络的高炉煤气预测方法 被引量:2
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作者 张廷坤 刘承宝 +3 位作者 穆塔里夫·阿赫迈德 谭杰 李经纬 樊智超 《烧结球团》 北大核心 2023年第6期90-100,108,共12页
现场充分利用高炉煤气(BFG)可有效降低一次能源的消耗,但高炉现场工况不断变化,煤气供需关系时刻处于不平衡状态,导致煤气放散现象仍然存在。为了提高煤气利用率,提出一种基于时序注意力(T-Attention)网络的BFG预测方法。该方法首先结... 现场充分利用高炉煤气(BFG)可有效降低一次能源的消耗,但高炉现场工况不断变化,煤气供需关系时刻处于不平衡状态,导致煤气放散现象仍然存在。为了提高煤气利用率,提出一种基于时序注意力(T-Attention)网络的BFG预测方法。该方法首先结合高炉冶炼机理和最大互信息系数(MIC)选取影响BFG含量的关键因素;然后针对采集数据中存在随机扰动,利用小波分析去除数据中的噪声;且在建模过程中,利用门控循环单元(GRU)捕捉多变量数据中周期性波动规律,同时融入注意力机制实时计算每个样本中各变量与预测值之间耦合关系并进行权重分配,提高模型动态自适应能力和解读性;最后利用某钢铁厂高炉现场数据进行验证。结果表明,T-Attention网络模型预测效果优于传统方法,能够准确预测BFG中的指标,为后期BFG调度以及节能减排提供及时准确的决策参考。 展开更多
关键词 高炉煤气 注意力机制 多元时间序列 神经网络 预测 CO CO_(2)
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基于多尺度特征融合与双注意力机制的多元时间序列预测 被引量:2
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作者 韩璐 霍纬纲 +1 位作者 张永会 刘涛 《计算机工程》 CAS CSCD 北大核心 2023年第9期99-108,共10页
多元时间序列的各子序列包含不同时间跨度的多尺度特征,现有时间序列预测模型不能有效地捕获多尺度特征以及评估其重要程度。提出一种基于多尺度时序特征融合与双注意力机制的多元时间序列预测网络FFANet,有效融合多尺度特征并关注其中... 多元时间序列的各子序列包含不同时间跨度的多尺度特征,现有时间序列预测模型不能有效地捕获多尺度特征以及评估其重要程度。提出一种基于多尺度时序特征融合与双注意力机制的多元时间序列预测网络FFANet,有效融合多尺度特征并关注其中重要部分。通过多尺度时序特征融合模块中并行的时序膨胀卷积层,使模型具有多种感受域,从而提取时序数据在不同尺度上的特征,并根据重要性对其进行自适应融合。利用双注意力模块对融合的时序特征进行重新标定,通过分配时序和通道注意力权重并加权至对应的时序特征,使FFANet聚焦对预测有重要贡献的特征。实验结果表明,相比AR、VARMLP、RNN-GRU、LSTNet-skip、TPA-LSTM、MTGNN和AttnAR时间序列预测模型,FFANet在Traffic、Solar Energy和Electricity数据集上的RRSE预测误差分别平均降低0.1523、0.1200、0.0743、0.0354、0.0215、0.0121、0.0200。 展开更多
关键词 多元时间序列预测 卷积神经网络 多尺度特征 特征融合 注意力机制
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