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Search Results: 1 - 10 of 310476 matches for " CPI,季节性ARIMA,预测<br>CPI "
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云南省CPI序列的分析与预测—基于SARIMA模型
Analysis and Prediction of Yunnan CPI Series—Based on SARIMA Model
 [PDF]

李卓然, 孙晓宇
Statistics and Applications (SA) , 2016, DOI: 10.12677/SA.2016.52015
Abstract: 本文以云南省为例,运用近20年来的月度数据对CPI进行建模预测。分析表明,CPI数据呈现周期为12的季节性;文章通过建立季节性ARIMA模型,预测2016年第二季度云南省CPI将在第一季度的基础上逐渐上升,且能够保持在稳定增长的范围内。
Taking Yunnan Province as an example, monthly data nearly 20 years were used on CPI forecast modeling. Analysis shows that the CPI data present seasonal cycle of 12. Through the establishment of the seasonal ARIMA models in this article, we predict the CPI of Yunnan Province in 2016 in the second quarter will gradually rise on the basis of the first quarter. And it is able to maintain within the scope of stable growth.
基于条件植被温度指数的冬小麦产量预测
田苗,王鹏新,张树誉,刘峻明,景毅刚,李俐
农业机械学报 , 2014, DOI: 10.6041/j.issn.1000-1298.2014.02.040
Abstract: 条件植被温度指数(VTCI)综合了地表主要参数——植被指数(NDVI)和地表温度(LST),能够较为准确地对干旱进行监测,可为抗旱救灾、遥感作物估产等提供科学依据。在改进层次分析法的加权VTCI与冬小麦产量的相关性研究成果和VTCI的季节性ARIMA模型干旱预测研究成果基础上,对关中平原的冬小麦产量进行向前1旬、2旬和3旬的预测研究。研究结果表明,产量预测结果与产量监测结果吻合较好,预测精度随着预测步长的增大而降低,关中平原4个地级市平均产量预测结果的最大相对误差为3.27%,说明用该方法可以进行向前3旬的产量预测。
Season tendency superposing——Markov forecasting model and its application
季节性叠加趋势-马尔柯夫预测模型及其应用

FAN Xiao-qing,JIANG Lu-lu,TAN Li-hong,HE Yong,
范小青
,蒋璐璐,谈黎虹,何勇

浙江大学学报(农业与生命科学版) , 2008,
Abstract: 为提高利用季节性叠加趋势模型预测有较大波动性数据序列的预测精度,提出一种季节性叠加趋势—马尔柯夫组合预测新方法,并用于油菜平均产量的预测.采用浙江省诸暨市1949年到1996年的油菜平均每公顷产量数据建立一个季节性叠加趋势—马尔柯夫组合预测模型,对1997年到2003年的油菜平均每公顷产量进行预测,预测精度分别为:97·9%、97·9%、97·9%、97·9%、98·8%、97·7%和98·4%,远远高于季节性叠加趋势模型的预测精度:76·1%、68·9%、70·9%、97·9%、82·5%、76·9%和82·2%.该方法具有计算简单、精度高的特点.说明利用季节性叠加趋势—马尔柯夫组合预测模型可以大大提高具有周期趋势性和较大波动性数据序列的预测精度.
Selection of characteristic components for geomagnetic matching based on statistical modeling
基于统计建模的地磁匹配特征量选择

齐玮,王秀芳,李夕海,刘代志
地球物理学进展 , 2010,
Abstract: 本文从地磁导航的实际需要出发,提出了地磁场平静变化建模与地磁匹配特征量的选择问题,并通过季节性ARIMA模型对地磁场的7个分量分别进行了建模.从建模结果来看,如果能够解决调水平精度的问题,Z分量和I分量是最优良的地磁匹配分量;如果调水平的精度达不到要求,那么F分量不失为一种明智的选择.另外,本文还提出了地磁场最小扰动分量的概念,并用主成分分析的方法对此进行了初步的尝试.
Research on Combined Grey Neural Network Model of Seasonal Forecast
季节性预测的组合灰色神经网络模型研究

XING Mian,
邢棉

系统工程理论与实践 , 2001,
Abstract: Because the seasonal time sequence has the double trends of increasing and fluctuating, it is proposed for the combined grey neural network model of seasonal forecast. We study the problem of complex seasonal forecast with double nonlinear trends and give on application case. We provide a new and effective method for the seasonal forecast.
WTO时代CPI与PPI间影响力研究
Research on the Influence between CPI and PPI in Times of WTO
 [PDF]

李伟娟
Finance (FIN) , 2011, DOI: 10.12677/fin.2011.11003
Abstract: 2001年12月11日,我国正式成为WTO成员国,自此,我国的进出口贸易将逐步与国际市场接轨,影响着我国CPI与PPI之间的影响力。以WTO时代CPI与PPI的波动态势为基础,本文运用统计学方法得出结论:CPI是影响PPI波动的原因,而PPI不是引发CPI变动的原因。最后,剖析新现象的诱因并提出建议。
On December 11, 2001, China officially became the WTO member,since then, our country's import and export trades will have communication with the international market gradually, which affects the influence between CPI and PPI. Based on the volatility trend of CPI and PPI, The paper uses statistical methods to get the conclusions that CPI is the reason for the change of PPI and PPI is not the reason for the change of CPI. At last, the paper analyzes the reason for this new phenomenon and makes recommendations.
Prediction and Countermeasure of Chinese CPI Based on BP Neural Network
基于BP神经网络的我国CPI预测与对策

WANG Yu,LI Xu-dong,LI Zi-li,
王宇
,李旭东,李自力

计算机科学 , 2009,
Abstract: Since 2007,CPI in our country has reached new high repeatedly.Using data published by State Statistical Bureau,and processing them,we applied BP neural network with momentum item to forecast separately CPI in 2008 and in 2009 will be respectively 104.91 and 104.88,CPI in the first quarter and second quarter of 2008 is respectively 106.36 and 106.53,CPI for food classification will be respectively 116.52 and 116.32,and also put forward some corresponding policy proposals.
基于奇异谱和混沌支持向量机模型预测三峡水库月径流
Monthly Runoff Prediction Based on Singular Spectrum Analysis and Chaotic Support Vector Machines
 [PDF]

汪芸, 郭生练, 曹广晶, 鲍正风
Journal of Water Resources Research (JWRR) , 2012, DOI: 10.12677/JWRR.2012.13011
Abstract: 径流时间序列在一定程度上可看作是一种被噪声污染的一些准周期信号的组合。为了提高径流预测精度,利用奇异谱分析方法对三峡水库1882~2010的入库径流资料进行预处理,得到重建序列,并分别运用季节性一阶自回归、小波神经网络和混沌支持向量机模型对原始和重建序列进行模拟预测,分析比较三个模型的预测精度。结果表明奇异谱分析法不仅可以浓缩主要信息和减小误差,也能够明显地提高月径流预测精度;基于奇异谱分析方法的混沌支持向量机模型的模拟预测精度最高,检验期模型的确定性系数高达86.9%,年均最大、最小月径流相对误差分别为9%7%
The runoff time series is often assumed as a combination of quasi-periodic signals contaminated by noises to some extent. The singular spectrum analysis method is used to preprocess inflow data of the Three Gorges Reservoir from 1882 to 2010 and a new reconstructed sequence is obtained. The seasonal autoregressive model, wavelet neural network model and chaotic support vector machines are applied to simulate and predict the original and reconstructed inflow data series. The results show that the singular spectrum analysis method for data preprocessing not only can concentrate the main information and reduce errors, but also significantly improve the accuracy of the monthly runoff prediction. The chaotic support vector machines coupled with singular spectrum analysis performs best among these models, which determination coefficient (DC) attains to 86.9%, relative errors of annual average maximum monthly inflow (REmax) and minimum monthly inflow (REmin) are equal to 9% and –7% during the testing period, respectively.
SSA-LSSVM在中长期径流预测中的应用研究
Application of SSA-LSSVM in Mid-Long Term Runoff Prediction
 [PDF]

巴欢欢, 郭生练, 钟逸轩, 刘章君
Journal of Water Resources Research (JWRR) , 2016, DOI: 10.12677/JWRR.2016.55049
Abstract:
为提高中长期径流预测精度,利用奇异谱分析(SSA)对输入资料进行数据预处理,消除噪声,得到重建序列。以水布垭水库1951~2009年的入库月径流资料为依据,选用季节性一阶自回归模型、支持向量机模型和最小二乘支持向量机模型作为径流预测模型,对原始序列和重建序列进行模拟预测。结果表明,基于奇异谱分析的最小二乘支持向量机的模拟预测精度最高,率定期和检验期的模型效率系数分别高达89%和84%。说明采用SSA对资料进行预处理可以显著提高中长期径流预报的精度。
To improve the accuracy of runoff prediction, Singular Spectrum Analysis (SSA) is applied to preprocess the original flow series and a new reconstructed series is obtained. The monthly inflow data of the Shui-buya Reservoir from 1951 to 2009 were selected as a case study. Seasonal Autoregressive (SAR) model, support vector machine (SVM) and least square support vector machine (LSSVM) are used to simulate and predict the original and reconstructed data series. The results show that SSA-LSSVM performs the best among these models, in which the model efficiency coefficients reach 89% and 84% during the verification and testing periods, respectively. It is shown that the accuracy of mid-long term runoff prediction can be significantly improved by using SSA.
布伦特原油价格季节性波动分析
王书平, 吴振信
中国管理科学 , 2008,
Abstract: ?油价时间序列往往受众多因素的影响,从而可以分解成各种成分。本文利用X-12-ARIMA方法分析布伦特原油价格的季节性波动,探讨油价运动规律,为我国进口石油提供决策支持。
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