共查询到19条相似文献,搜索用时 671 毫秒
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模糊神经网络和SARIMA模型分别对非线性和线性时间序列有很好的预测能力,但在实际应用中大多数序列并非稳定、单纯线性或非线性的。为了提高预测精度,提出了一种基于T-S模糊神经网络与SARIMA结合的时间序列预测模型。针对悉尼航班乘客收入数据给出了三种混合模型,并与模糊神经网络、支持向量机、SARIMA和BP神经网络四种单独模型进行比较。实验结果表明,从预测精度和参数选择方面来看,所给模型是有效的。 相似文献
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模糊神经网络与SARIMA结合的时间序列预测模型 总被引:1,自引:0,他引:1
模糊神经网络和SARIMA模型分别对非线性和线性时间序列有很好的预测能力,但在实际应用中大多数序列并非稳定、单纯线性或非线性的.为了提高预测精度,提出了一种基于T-S模糊神经网络与SARIMA结合的时间序列预测模型.针对悉尼航班乘客收入数据给出了三种混合模型,并与模糊神经网络、支持向量机、SARIMA和BP神经网络四种单独模型进行比较.实验结果表明,从预测精度和参数选择方面来看,所给模型是有效的. 相似文献
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针对BP神经网络学习算法随机初始化连接权值和阈值易使模型陷入局部极小点的缺点,设计了一种自适应差分进化算法优化BP神经网络的混合算法。该混合算法中,差分进化算法采用自适应变异和交叉因子优化BP神经网络的初始权值和阈值,再用预寻优得到的初始权值和阈值训练BP神经网络得到最优的权值和阈值。首先对改进的自适应差分进化算法运用测试函数进行性能测试,然后用一个经典时间序列问题对提出的混合算法进行了检验,并与一般的神经网络、ARIMA预测模型及其它混合预测模型进行了对比,实验结果表明,本文提出的混合算法有效并且明显提高了预测精度。 相似文献
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针对BP神经网络预测模型收敛速度慢和容易陷入局部极小值的缺点,将差分进化算法和神经网络结合起来,提出了一种基于差分进化算法的BP神经网络预测混沌时间序列的方法,利用差分进化算法的全局寻优能力对BP神经网络的权值和阈值进行优化,然后训练BP神经网络预测模型求得最优解,将该预测方法用到3个典型的混沌时间序列进行算法的有效性验证,并与BP算法的预测精度进行了比较,仿真结果表明该方法对混沌时间序列预测具有更好的非线性拟合能力和更高的预测准确性。 相似文献
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基于时间序列的支持向量机在股票预测中的应用 总被引:1,自引:0,他引:1
由于股票预测是不确定、非线性、非平稳的时间序列问题,传统的方法往往难以取得满意的预测效果。本文提出一种基于时间序列的支持向量机(SVM)股票预测方法。利用沙河股份的股票数据,建立股票收盘价回归预测模型,该模型克服了传统时间序列预测模型仅局限于线性系统的情况。实验结果表明,该方法比神经网络方法以及时间序列方法的预测精度更高,可以很好的应用某些非线性时间序列的预测中。 相似文献
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针对目前混沌时间序列预测模型预测结果差异较大的问题,归纳了4种混沌时间序列预测模型:BRF神经网络模型、最大Lyapunov指数模型、局域线性模型和Volterra滤波器自适应预测模型,并对这4种预测模型进行了比较研究。应用4种预测模型对几个典型的非线性系统进行预测仿真。结果表明,这4种预测模型对典型混沌时间序列预测都具有很好的预测效果;在预测精度上BRF模型和Volterra模型明显优于最大Lyapunov指数模型和局域线性模型。 相似文献
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季节性组合预测模型在医院门诊量中的应用研究 总被引:5,自引:0,他引:5
医院门诊量是一个具有复杂的非线性组合特征的季节性时问序列。为解决传统时间序列预测大多数都是使用单一模型,以致影响预测精度等问题,采用了最优加权组合预测方法将季节性ARIMA乘积模型和季节性神经网络模型进行组合优化。结果表示,季节性组合预测模型在拟合精度和预测准确性方面优于任何单一预测方法,为季节性时间序列预测提供了一种新的实用方法。 相似文献
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《计算机应用与软件》2016,(4)
针对混沌时间序列的混沌性,提出一种改进的相空间重构方法——交集寻优法;针对传统的BP神经网络、RBF神经网络及AR模型对混沌时间序列预测效率和预测精度较低的缺点,提出两种不同的Hermite神经网络预测模型。以四阶蔡氏电路为模型,结合粒子群算法建立预测模型。仿真结果表明,利用交集寻优法进行相空间重构能很好地保留原系统的动力学特性,证实了该方法的有效性;Hermite神经网络较传统的预测模型精度更高,便于基于粒子群算法的Hermite神经网络预测方法的推广和应用。 相似文献
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为捕捉时间序列中潜在的特征依赖关系并实现高维时序数据的快速模糊预测,构建基于时间卷积网络(TCN)与自注意力机制的两种混合网络模型:TSANet和TSANet-MF。TSANet模型通过全局和局部两个并行卷积分量结构提取特征后,利用自注意力机制增强特征点关联程度,并结合并行的TCN增大卷积的感受野范围,最大程度地捕捉多维时序数据的周期性特征。TSANet-MF模型将TSANet作为矩阵分解算法的正则化项,使高维数据转化为具有更多时序特征的低维数据,减少计算复杂度,实现高维数据的快速模糊预测。在4种不同领域的时间序列数据集上的实验结果表明,TSANet模型在3种数据集上的预测性能均优于基准模型,尤其在高维Traffic数据集上相对平方根误差降低了19.52%~56.37%,TSANet-MF模型在Electricity和Traffic高维数据集上的训练时间相比于基准模型明显减少。上述实验结果验证了两种混合网络模型均具有较好的多维时间序列预测性能。 相似文献
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A rapid learning and dynamic stepwise updating algorithm for flatneural networks and the application to time-series prediction 总被引:3,自引:0,他引:3
Chen C.L.P. Wan J.Z. 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》1999,29(1):62-72
A fast learning algorithm is proposed to find an optimal weights of the flat neural networks (especially, the functional-link network). Although the flat networks are used for nonlinear function approximation, they can be formulated as linear systems. Thus, the weights of the networks can be solved easily using a linear least-square method. This formulation makes it easier to update the weights instantly for both a new added pattern and a new added enhancement node. A dynamic stepwise updating algorithm is proposed to update the weights of the system on-the-fly. The model is tested on several time-series data including an infrared laser data set, a chaotic time-series, a monthly flour price data set, and a nonlinear system identification problem. The simulation results are compared to existing models in which more complex architectures and more costly training are needed. The results indicate that the proposed model is very attractive to real-time processes. 相似文献
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针对目前非线性动态偏最小二乘(PLS)建模方法在拟合较强非线性化工过程时存在的问题, 提出一种基于稳定学习的递归神经网络动态PLS建模方法. 该算法将递归神经网络与Hammerstein模型相结合, 对外部PLS提取的特征向量进行内部建模, 具有逼近较强非线性化工过程的能力, 改善了模型的适用范围. 此外, 采用带有稳定学习的参数更新算法对模型参数进行在线修正, 改善了模型的预测精度和自适应能力. 将此方法应用于氧化铝生产过程铝酸钠溶液组分浓度建模实验, 仿真结果表明, 本方法是可行有效的. 相似文献
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《Computers & Operations Research》2001,28(4):381-396
This study presents an experimental evaluation of neural networks for nonlinear time-series forecasting. The effects of three main factors — input nodes, hidden nodes and sample size, are examined through a simulated computer experiment. Results show that neural networks are valuable tools for modeling and forecasting nonlinear time series while traditional linear methods are not as competent for this task. The number of input nodes is much more important than the number of hidden nodes in neural network model building for forecasting. Moreover, large sample is helpful to ease the overfitting problem.Scope and purposeInterest in using artificial neural networks for forecasting has led to a tremendous surge in research activities in the past decade. Yet, mixed results are often reported in the literature and the effect of key modeling factors on performance has not been thoroughly examined. The lack of systematic approaches to neural network model building is probably the primary cause of inconsistencies in reported findings. In this paper, we present a systematic investigation of the application of neural networks for nonlinear time-series analysis and forecasting. The purpose is to have a detailed examination of the effects of certain important neural network modeling factors on nonlinear time-series modeling and forecasting. 相似文献
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《Computers & Operations Research》2001,28(12):1183-1202
This study examines the capability of neural networks for linear time-series forecasting. Using both simulated and real data, the effects of neural network factors such as the number of input nodes and the number of hidden nodes as well as the training sample size are investigated. Results show that neural networks are quite competent in modeling and forecasting linear time series in a variety of situations and simple neural network structures are often effective in modeling and forecasting linear time series.Scope and purposeNeural network capability for nonlinear modeling and forecasting has been established in the literature both theoretically and empirically. The purpose of this paper is to investigate the effectiveness of neural networks for linear time-series analysis and forecasting. Several research studies on neural network capability for linear problems in regression and classification have yielded mixed findings. This study aims to provide further evidence on the effectiveness of neural network with regard to linear time-series forecasting. The significance of the study is that it is often difficult in reality to determine whether the underlying data generating process is linear or nonlinear. If neural networks can compete with traditional forecasting models for linear data with noise, they can be used in even broader situations for forecasting researchers and practitioners. 相似文献
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