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Wind-speed forecasting model based on DBN-Elman combined with improved PSO-HHT
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作者 Wei Liu Feifei Xue +4 位作者 Yansong Gao Wumaier Tuerxun Jing Sun Yi Hu Hongliang Yuan 《Global Energy Interconnection》 EI CSCD 2023年第5期530-541,共12页
Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel predictio... Random and fluctuating wind speeds make it difficult to stabilize the wind-power output,which complicates the execution of wind-farm control systems and increases the response frequency.In this study,a novel prediction model for ultrashort-term wind-speed prediction in wind farms is developed by combining a deep belief network,the Elman neural network,and the Hilbert-Huang transform modified using an improved particle swarm optimization algorithm.The experimental results show that the prediction results of the proposed deep neural network is better than that of shallow neural networks.Although the complexity of the model is high,the accuracy of wind-speed prediction and stability are also high.The proposed model effectively improves the accuracy of ultrashort-term wind-speed forecasting in wind farms. 展开更多
关键词 Wind-speed forecasting DBN ELMAN HHT combined neural network
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Nonlinear combined forecasting model based on fuzzy adaptive variable weight and its application 被引量:1
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作者 蒋爱华 梅炽 +1 位作者 鄂加强 时章明 《Journal of Central South University》 SCIE EI CAS 2010年第4期863-867,共5页
In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using concept... In order to enhance forecasting precision of problems about nonlinear time series in a complex industry system,a new nonlinear fuzzy adaptive variable weight combined forecasting model was established by using conceptions of the relative error,the change tendency of the forecasted object,gray basic weight and adaptive control coefficient on the basis of the method of fuzzy variable weight.Based on Visual Basic 6.0 platform,a fuzzy adaptive variable weight combined forecasting and management system was developed.The application results reveal that the forecasting precisions from the new nonlinear combined forecasting model are higher than those of other single combined forecasting models and the combined forecasting and management system is very powerful tool for the required decision in complex industry system. 展开更多
关键词 nonlinear combined forecasting nonlinear time series method of fuzzy adaptive variable weight relative error adaptive control coefficient
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A combined forecasting method for intermittent demand using the automotive aftermarket data 被引量:1
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作者 Xiaotian Zhuang Ying Yu Aihui Chen 《Data Science and Management》 2022年第2期43-56,共14页
Intermittent demand forecasting is an important challenge in the process of smart supply chain transformation,and accurate demand forecasting can reduce costs and increase efficiency for enterprises.This study propose... Intermittent demand forecasting is an important challenge in the process of smart supply chain transformation,and accurate demand forecasting can reduce costs and increase efficiency for enterprises.This study proposes an intermittent demand combination forecasting method based on internal and external data,builds intermittent demand feature engineering from the perspective of machine learning,predicts the occurrence of demand by classification model,and predicts non-zero demand quantity by regression model.Based on the strategy selection on the inventory side and the stocking needs on the replenishment side,this study focuses on the optimization of the classification problem,incorporates the internal and external data of the enterprise,and proposes two combination forecasting optimization methods on the basis of the best classification threshold searching and transfer learning,respectively.Based on the real data of auto after-sales business,these methods are evaluated and validated in multiple dimensions.Compared with other intermittent forecasting methods,the models proposed in this study have been improved significantly in terms of classification accuracy and forecasting precision,which validates the potential of combined forecasting framework for intermittent demand and provides an empirical study of the framework in industry practice.The results show that this research can further provide accurate upstream inputs for smart inventory and guarantee intelligent supply chain decision-making in terms of accuracy and efficiency. 展开更多
关键词 Intelligent supply chain management Intermittent demand combination forecasting Machine learning Transfer learning
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A New Multi-Method Combination Forecasting Model for ESDD Predicting
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作者 Haiyan SHUAI Qingwu GONG 《Energy and Power Engineering》 2009年第2期94-99,共6页
Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. The precise ESDD forecasting plays an important role in the safety, economy and reliability of... Equal Salt Deposit Density (ESDD) is a main factor to classify contamination severity and draw pollution distribution map. The precise ESDD forecasting plays an important role in the safety, economy and reliability of power system. To cope with the problems existing in the ESDD predicting by multivariate linear regression (MLR), back propagation (BP) neural network and least squares support vector machines (LSSVM), a nonlinear combination forecasting model based on wavelet neural network (WNN) for ESDD is proposed. The model is a WNN with three layers, whose input layer has three neurons and output layer has one neuron, namely, regarding the ESDD forecasting results of MLR, BP and LSSVM as the inputs of the model and the observed value as the output. In the interest of better reflection of the influence of each single forecasting model on ESDD and increase of the accuracy of ESDD prediction, Morlet wavelet is used to con-struct WNN, error backpropagation algorithm is adopted to train the network and genetic algorithm is used to determine the initials of the parameters. Simulation results show that the accuracy of the proposed combina-tion ESDD forecasting model is higher than that of any single model and that of traditional linear combina-tion forecasting (LCF) model. The model provides a new feasible way to increase the accuracy of pollution distribution map of power network. 展开更多
关键词 equal salt deposit density MULTIVARIATE linear regression BP NEURAL NETWORK least SQUARES support vector machines combination forecasting wavelet NEURAL NETWORK
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Combined SAI-SHAO prediction of Earth Orientation Parameters since 2012 till 2017
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作者 Leonid Zotov Xueqing Xu +1 位作者 Yonghong Zhou Arkadiy Skorobogatov 《Geodesy and Geodynamics》 2018年第6期485-490,共6页
As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have a... As the participants of Earth Orientation Parameters Combination of Prediction Pilot Project(EOPC PPP),Sternberg Astronomical Institute of Moscow State University(SAI) and Shanghai Astronomical Observatory(SHAO) have accumulated ~1800 days of Earth Orientation Parameters(EOP) predictions since2012 till 2017, which were up to 90 days into the future, and made by four techniques: auto-regression(AR), least squares collocation(LSC), and neural network(NNET) forecasts from SAI, and least-squares plus auto-regression(LS+AR) forecast from SHAO. The predictions were finally combined into SAISHAO COMB EOP prediction. In this work we present five-year real-time statistics of the combined prediction and compare it with the uncertainties of IERS bulletin A predictions made by USNO. 展开更多
关键词 EOP prediction Error estimation combined forecast Polar motion UT1-UTC
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High levels of Zinc-α-2-Glycoprotein among Omani AIDS patients on combined antiretroviral therapy
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作者 Sidgi Syed Anwer Hasson Mohammed Saeed Al-Balushi +6 位作者 Muzna Hamed Al Yahmadi Juma Zaid Al-Busaidi Elias Antony Said Mohammed Shafeeq Othman Talal Abdullah Sallam Mohammed Ahmad Idris Ali Abdullah Al-Jabri 《Asian Pacific Journal of Tropical Biomedicine》 SCIE CAS 2014年第8期610-613,共4页
Objective:To investigate the levels of zinc-α-2-glycoprotein(ZAG) among Omani AIDS patients receiving combined antiretroviral therapy(cART).Methods:A total of 80 Omani AIDS patients(45 males and 33 females),average a... Objective:To investigate the levels of zinc-α-2-glycoprotein(ZAG) among Omani AIDS patients receiving combined antiretroviral therapy(cART).Methods:A total of 80 Omani AIDS patients(45 males and 33 females),average age of 36 vears.who were receiving cART at the Saltan Qaboos University Hospital(SQUH).Muscat,Oman,were tested for the levels of ZAG.In addition,SO healthy blood donors(46 males and 34 females),average age of 26 years,attending the SOUH Blood Bank,were tested in parallel as a control group.Measurement of the ZAG levels was performed using a competitive enzyme—linked immunosorbent assay and in accordance with the manufacturer's instructions.Results:The ZAG levels were found to he significantly higher among AIDS patients compared to the healthy individuals(P=0.033).A total of 56(70%) of the AIDS patients were found to have higher levels of ZAG and 16(20%) AIDS patients were found to have high ZAG levels,which are significantly(P>0.031) associated with weight loss.Conclusions:ZAG levels are high among Omani AIDS patients on cART and this necessitales the measurement of ZAG on routine basis,as it is associated with weight loss. 展开更多
关键词 Zinc-α-2-glycoprotein AIDS Patients combineD ANTIRETROVIRAL therapy Levels Oman
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Effect of TCM Combined with Chemotherapy on Immune Function and Quality of Life of Patients with Non-small Cell Lung Cancer inStage Ⅲ-Ⅳ
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作者 杨祖贻 吴雪梅 +3 位作者 欧亚龙 余萍 罗洁 宋秀云 《Chinese Journal of Integrated Traditional and Western Medicine》 SCIE CAS 2004年第3期181-186,共6页
Objective: To observe and compare the effect of traditional Chinese medicine (TCM) combined with chemotherapy (CT) on immune function and quality of life (QOL)of patients with non-small cell lung cancer (NSCLC) in sta... Objective: To observe and compare the effect of traditional Chinese medicine (TCM) combined with chemotherapy (CT) on immune function and quality of life (QOL)of patients with non-small cell lung cancer (NSCLC) in stage Ⅲ-Ⅳ. Methods: One hundred cases with stage Ⅲ-Ⅳ NSCLC were randomly divided into two groups. The treated group (n=50) received CT combined with TCM, and the control group received CT alone. The percentage of T lymphocyte subset in peripheral blood and the change of natural killer (NK) cell count were observed after treatment. The QOL and tolerance of CT were also compared between the two groups after treatment. Results: In the treated group, CD3 cell count, CD4 cell count, CD4/ CDg ratio and NK cell activity were higher than those in control group, while CD8 cell count in the treated group was lower than that in the control group (P<0.05), and QOL and tolerance of CT in the treated group were also better (P<0.05). Conclusion: TCM combined with CT could raise the patients' ability in tolerating CT in stage Ⅲ-ⅣNSCLC. 展开更多
关键词 non-small cell lung cancer in stage - traditional Chinese medicine combined with chemotherapy immune function quality of life
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IMPACT OF SUMMER WARMING ON DYNAMICS-STATISTICS-COMBINED METHOD TO PREDICT THE SUMMER TEMPERATURE IN CHINA
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作者 苏海晶 乔少博 +1 位作者 杨杰 王晓娟 《Journal of Tropical Meteorology》 SCIE 2017年第4期440-449,共10页
Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China's summer observed temperature in 1983—2012. Moreover, a dynamics-statist... Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China's summer observed temperature in 1983—2012. Moreover, a dynamics-statistics-combined seasonal forecast method with optimal multi-factor portfolio is applied to analyze the impact of this trend on summer temperature forecast. The results show that: in the three decades, the summer temperature shows a clear upward trend under the condition of global warming, especially over South China, East China, Northeast China and Xinjiang Region, and the trend rate of national average summer temperature was 0.27℃ per decade. However, it is found that the current business model forecast(Coupled Global Climate Model) of National Climate Centre is unable to forecast summer warming trends in China, so that the post-processing forecast effect of dynamics-statistics-combined method is relatively poor. In this study, observed temperatures are processed first by removing linear fitting trend, and then adding it after forecast to offset the deficiency of model forecast indirectly. After test, ACC average value in the latest decade was 0.44 through dynamics-statistics-combined independent sample return forecast. The temporal correlation(TCC) between forecast and observed temperature was significantly improved compared with direct forecast results in most regions, and effectively improved the skill of the dynamics-statistics-combined forecast method in seasonal temperature forecast. 展开更多
关键词 dynamics-statistics-combined global warming temperature forecast model error correction
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基于GA-QPSO-ELM的边坡位移组合预测
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作者 傅嘉辉 张夫龙 +1 位作者 张学超 闫少霞 《自动化技术与应用》 2025年第1期53-56,共4页
为了提高水利工程边坡位移预测精度,在QPSO算法寻优过程中引入遗传算法的交叉和变异操作,形成GA-QPSO算法。采用GA-QPSO算法对ELM参数进行优化,建立基于GA-QPSO-ELM的边坡位移组合预测模型,采用实际水利工程的边坡位移监测数据进行仿真... 为了提高水利工程边坡位移预测精度,在QPSO算法寻优过程中引入遗传算法的交叉和变异操作,形成GA-QPSO算法。采用GA-QPSO算法对ELM参数进行优化,建立基于GA-QPSO-ELM的边坡位移组合预测模型,采用实际水利工程的边坡位移监测数据进行仿真分析,并与其他边坡位移预测方法进行对比。结果表明,GA-QPSO-ELM组合模型的平均相对误差为1.186%,预测精度高于其他方法,验证了模型的正确性和优越性。 展开更多
关键词 边坡位移 组合预测 极限学习机 遗传算法 量子粒子群算法
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基于VMD-改进最优加权法的短期负荷变权组合预测策略 被引量:1
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作者 李志军 徐博 +1 位作者 杨金荣 宁阮浩 《国外电子测量技术》 2024年第2期1-8,共8页
为提升短期电力负荷预测精度,提出了一种变权组合预测策略。首先,为了降低负荷数据的不平稳度,使用变分模态分解(variational mode decomposition,VMD)将负荷数据分解成了高频、低频、残差3种特征模态分量。其次,充分计及负荷数据的时... 为提升短期电力负荷预测精度,提出了一种变权组合预测策略。首先,为了降低负荷数据的不平稳度,使用变分模态分解(variational mode decomposition,VMD)将负荷数据分解成了高频、低频、残差3种特征模态分量。其次,充分计及负荷数据的时序特点,参考指数加权法原理设计自适应误差重要性量化函数,并结合组合模型在时间窗口内的历史负荷数据的均方预测误差设计改进最优加权法的目标函数和约束条件,以完成子模型的准确变权。最后,针对波动较强的高频分量选定极端梯度提升(XGBoost)和卷积神经网络-长短期记忆(CNN-LSTM)模型并使用改进最优加权法进行组合预测、低频分量使用多元线性回归(MLR)模型预测、残差分量使用LSTM模型预测,叠加各模态分量的预测结果,实现了短期负荷数据的准确预测。实验结果表明,使用策略组合模型的平均绝对百分比误差为4.18%。与使用传统组合策略的组合模型相比,平均绝对百分比预测误差平均降低了0.87%。 展开更多
关键词 短期负荷预测 变分模态分解 改进最优加权法 组合模型
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基于CEEMDAN-GRU组合模型的碳排放交易价格预测研究
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作者 傅魁 钱素彬 徐尚英 《武汉理工大学学报(信息与管理工程版)》 CAS 2024年第1期62-66,共5页
准确的碳价格预测有助于监管部门观测碳交易市场运行状况及投资者进行科学决策,对实现碳达峰和碳中和具有重要作用。但碳价序列具有非线性、非平稳性和高噪声的特性,很难对其进行准确预测。将完全自适应噪声集合经验模态分解(CEEMDAN)... 准确的碳价格预测有助于监管部门观测碳交易市场运行状况及投资者进行科学决策,对实现碳达峰和碳中和具有重要作用。但碳价序列具有非线性、非平稳性和高噪声的特性,很难对其进行准确预测。将完全自适应噪声集合经验模态分解(CEEMDAN)方法与门控循环单元(GRU)相结合,构建一个碳排放交易价格预测模型。该模型基于分解、集成思想,利用CEEMDAN将原始碳价序列分解,获得不同频率的本征模函数(IMF)和残差序列,使用GRU神经网络分别为各子序列建立预测模型,最后集成预测结果得到碳价预测值。以湖北省碳交易市场的日度成交价为例进行实证分析,结果表明:相较于其他5种基准模型,CEEMDAN-GRU模型具有更小的预测误差和更高的拟合优度,在碳价格预测上具有一定的优势。 展开更多
关键词 碳价格预测 组合模型 CEEMDAN GRU 机器学习
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SARIMA-GRU组合模型的水位预测
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作者 曹寒问 陈九江 李小玲 《南昌工程学院学报》 CAS 2024年第3期8-12,共5页
相较于传统的单一模型,组合模型在一定条件下具有更优的预测精度。为验证组合模型是否有利于提高模型的预测精度,本文以长江中游支流澧水石龟山水电站的水位数据为基础,建立SARIMA模型和GRU神经网络模型,并将这两种模型基于方差倒数法和... 相较于传统的单一模型,组合模型在一定条件下具有更优的预测精度。为验证组合模型是否有利于提高模型的预测精度,本文以长江中游支流澧水石龟山水电站的水位数据为基础,建立SARIMA模型和GRU神经网络模型,并将这两种模型基于方差倒数法和IOWA算子进行组合,最后比较单一模型和组合模型在该水位数据集上的预测精度差异。结果表明,适当的组合方式有利于提高模型预测精度,基于IOWA算子的组合模型具优良的预测性能。 展开更多
关键词 SARIMA GRU神经网络 水位预测 组合模型
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Forecasting China’s natural gas consumption based on a combination model 被引量:10
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作者 Gang Xu Weiguo W ang 《Journal of Natural Gas Chemistry》 EI CAS CSCD 2010年第5期493-496,共4页
Ensuring a sufficient energy supply is essential to a country. Natural gas constitutes a vital part in energy supply and therefore forecasting natural gas consumption reliably and accurately is an essential part of a ... Ensuring a sufficient energy supply is essential to a country. Natural gas constitutes a vital part in energy supply and therefore forecasting natural gas consumption reliably and accurately is an essential part of a country's energy policy. Over the years, studies have shown that a combinative model gives better projected results compared to a single model. In this study, we used Polynomial Curve and Moving Average Combination Projection (PCMACP) model to estimate the future natural gas consumption in China from 2009 to 2015. The new proposed PCMACP model shows more reliable and accurate results: its Mean Absolute Percentage Error (MAPE) is less than those of any previous models within the investigated range. According to the PCMACP model, the average annual growth rate will increase for the next 7 years and the amount of natural gas consumption will reach 171600 million cubic meters in 2015 in China. 展开更多
关键词 natural gas consumption forecasting combination model
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Improvement of 6–15 Day Precipitation Forecasts Using a Time-Lagged Ensemble Method 被引量:4
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作者 JIE Weihua WU Tongwen +2 位作者 WANG Jun LI Weijing LIU Xiangwen 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2014年第2期293-304,共12页
A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions... A time-lagged ensemble method is used to improve 6-15 day precipitation forecasts from the Beijing Climate Center Atmospheric General Circulation Model,version 2.0.1.The approach averages the deterministic predictions of precipitation from the most recent model run and from earlier runs,all at the same forecast valid time.This lagged average forecast (LAF) method assigns equal weight to each ensemble member and produces a forecast by taking the ensemble mean.Our analyses of the Equitable Threat Score,the Hanssen and Kuipers Score,and the frequency bias indicate that the LAF using five members at time-lagged intervals of 6 h improves 6-15 day forecasts of precipitation frequency above 1 mm d-1 and 5 mm d-1 in many regions of China,and is more effective than the LAF method with selection of the time-lagged interval of 12 or 24 h between ensemble members.In particular,significant improvements are seen over regions where the frequencies of rainfall days are higher than about 40%-50% in the summer season; these regions include northeastern and central to southern China,and the southeastem Tibetan Plateau. 展开更多
关键词 time-lagged ensemble system lagged average forecast 6-15 day forecasts PRECIPITATION
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基于CNN-LSTM电力消耗预测模型及系统开发
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作者 龚立雄 钞寅康 +1 位作者 黄霄 陈佳霖 《计算机仿真》 2024年第8期77-83,共7页
有效预测电能负荷,对提高电力负荷时间序列测量准确度及合理制定用电能管理措施具有重要意义。针对传统预测模型在电能负荷预测中无法充分挖掘时间序列数据中隐藏特征的问题,基于电能数据时间序列的趋势,融合数值信息提出一种卷积神经网... 有效预测电能负荷,对提高电力负荷时间序列测量准确度及合理制定用电能管理措施具有重要意义。针对传统预测模型在电能负荷预测中无法充分挖掘时间序列数据中隐藏特征的问题,基于电能数据时间序列的趋势,融合数值信息提出一种卷积神经网络(convolutional neuralnetwork,CNN)与长期短期记忆循环神经网络(long short-term memory network,LSTM)相结合的混合多隐层CNN-LSTM电力能耗预测模型。首先,通过设定最小目标函数作为优化目标,Adam优化算法更新神经网络的权重,并对网络层和批大小进行自适应调优以确定最佳层数和批大小。其次,构建混合多隐层模型并进行隐层组合优化与讨论,确定最佳时间维度的参数,进行时间维度的特征学习进而预测下一时间序列的耗电量。然后以某公司的电力负荷数据为例进行验证,并与LSTM、CNN、RNN等模型的预测结果分析比较。结果表明上述混合多隐层模型预测准确度达98.94%,平均绝对误差(MAE)达到0.0066,均优于其他相关模型,证明以上混合预测模型在电力负荷预测精度方面具有更好的性能。基于上述理论,开发了能耗监控决策系统,实现设备状态实时监控和能耗智能预测功能,为解决传统制造业能耗需求不精确和能源库存浪费问题提供参考和指导。 展开更多
关键词 电力负荷预测 卷积神经网络 长短期记忆神经网络 混合多隐层组合模型
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The Optimal Weighted Combinational Forecasting with Constant Terms 被引量:1
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作者 ZHANG Jian-guo 《Chinese Quarterly Journal of Mathematics》 CSCD 北大核心 2007年第1期109-113,共5页
We propose a model based on the optimal weighted combinational forecasting with constant terms, give formulae of the weights and the average errors as well as a relation of the model and the corresponding model withou... We propose a model based on the optimal weighted combinational forecasting with constant terms, give formulae of the weights and the average errors as well as a relation of the model and the corresponding model without constant terms, and compare these models. Finally an example was given, which showed that the fitting precision has been enhanced. 展开更多
关键词 combinational forecasting constant term combinational weight fitting deviation
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Ice concentration assimilation in a regional ice-ocean coupled model and its application in sea ice forecasting 被引量:1
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作者 LI Qun ZHANG Zhanhai +1 位作者 SUN Li WU Huiding 《Advances in Polar Science》 2013年第4期258-264,共7页
A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational i... A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational ice forecast system based on a com- bined optimal interpolation and nudging scheme. The scheme produces a modeled sea ice concentration at every time step, based on the difference between observational and forecast data and on the ratio of observational error to modeled error. The impact and the effectiveness of data assimilation are investigated. Significant improvements to predictions of sea ice extent were obtained through the assimilation of ice concentration, and minor improvements through the adjustment of the upper ocean properties. The assimilation of ice thickness data did not significantly improve predictions. Forecast experiments show that the forecast accuracy is higher in summer, and that the errors on five-day forecasts occur mainly around the marginal ice zone. 展开更多
关键词 ice concentration assimilation combined optimal interpolation and nudging sea ice forecast skills core
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A New Type of Combination Forecasting Method Based on PLS——The Application of It in Cigarette Sales Forecasting 被引量:1
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作者 Biao Luo Liang Wan +1 位作者 Wei-Wei Yan Jie-Jie Yu 《American Journal of Operations Research》 2012年第3期408-416,共9页
Cigarette market is a kind of monopoly market which is closed loop running, it depends on the plan mechanism to schedule producing, supplying and selling, but the “bullwhip effect” still exists. So it has a fundamen... Cigarette market is a kind of monopoly market which is closed loop running, it depends on the plan mechanism to schedule producing, supplying and selling, but the “bullwhip effect” still exists. So it has a fundamental significance to do sales forecasting work. It needs to considerate the double trend characteristics, history sales data and other main factors that affect cigarette sales. This paper depends on the panel data of A province’s cigarette sales, first we established three single forecasting models, after getting the predicted value of these single models, then using the combination forecasting method which based on PLS to predict the province’s cigarette sales of the next year. The results show that the prediction accuracy is good, which could provide a certain reference to cigarette sales forecasting in A province. 展开更多
关键词 PLS ARMA Time Series METHOD combination forecasting METHOD SALES forecast
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Artificial Neural Network for Combining Forecasts
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作者 Shanming Shi, Li D. Xu & Bao Liu(Department of Computer Science, University of Colorado at Boulder, Boulder, CO 80309, USA)(Department of MSIS, Wright State University, Dayton, OH 45435,USA)(Institute of Systems Engineering, Tianjin University, Tianjin 30 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 1995年第2期58-64,共7页
This paper proposes artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, three forecasting models are used for individual forecasts, and then two linear combining methods a... This paper proposes artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, three forecasting models are used for individual forecasts, and then two linear combining methods are used to compare with the ANN combining method. The comparative experiment using real--world data shows that the prediction by the ANN method outperforms those by linear combining methods. The paper suggests that the ANN combining method can be used as- an alternative to conventional linear combining methods to achieve greater forecasting accuracy. 展开更多
关键词 Artificial neural network forecasting combined forecasts Nonlinear systems.
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Research on Methods of Parameter Estimation in Combining Forecasting Based on Harmonic Mean
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作者 Wang Yingming Dept. of Automation, Xiamen University, 361005, P. R. China 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 1998年第1期2-8,共7页
Two kinds of parameter estimation methods (I) and (II) of combining forecasting based on harmontic mean are proposed and compared through a lot of simulation forecasting examples. A very helpful conclusion is obtained... Two kinds of parameter estimation methods (I) and (II) of combining forecasting based on harmontic mean are proposed and compared through a lot of simulation forecasting examples. A very helpful conclusion is obtained, which can lay solid foundations for correct application of the above methods. 展开更多
关键词 Harmonic mean combining forecasting Parameter estimation.
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