BP神经网络在社会经济非线性时间序列预测中的应用

应用神经网络的基本原理,介绍了对非线性时间序列进行预测的方法。并通过社会经济系统实例验证该方法的科学合理性。     

威海地区年降水量的时间序列分析

通过应用时间序列分析技术,对威海地区1959-2002年降水量序列进行分析,证实年平均降水量序列满足ARIMA(1,1,(1,11))模型,并通过该模型对威海地区的2003-2007年降水量进行了预测分析。     

林产品价格的时间序列分析——以福州贮木场松杉原木价格为例

对福州贮木场松杉原木月价格进行时间序列分析,采用EViews5.0软件所做的实证分析结果表明:福州贮木场松杉原木月价格变化具有非平稳性和弱季节性特点,ARIMA模型对其价格序列具有良好的拟合效果,能够有效地预测其价格动态变化。     

林业经济预测模型库系统的设计与多模型组合研究

在综合前人林业经济预测模型和方法成果的基础上,研究开发了林业经济预测模型库系统。该系统有效地实现了模型的表示、生成及预测等功能,特别是多模型的灵活选择与组合,解决了多年来运用孤立模型进行预测的问题,提高了预测的效率与效果。此外,该系统的可视化界面方便了用户的使用。最后结合林业经济预测实例说明了多模型组合的应用及使用林业经济预测模型库系统进行预测的有效性。     

预测经济和金融时间序列:ARIMA与LSTM模型的比较

预测时间序列数据是经济学、商业和金融学中的一个重要主题。ARIMA模型在预测下一个时间序列滞后的精度和准确性方面表现出色。随着计算机计算能力的发展,产生了基于深度学习的新算法来预测时间序列数据,如长短期记忆(LSTM)。通过实证研究,基于深度学习的算法(如LSTM)优于传统算法,与ARIMA相比,LSTM获得的错误率平均降低在84%~87%之间,表明了LSTM对ARIMA的优越性。     

黄河流域农业灰水足迹与经济增长的关系研究——基于脱钩及回弹效应

目的 科学评价水资源环境及其与经济增长之间的矛盾,直接关系到黄河流域水资源可持续利用与农业经济高质量发展。方法 文章以灰水足迹为视角,纳入种植业和畜牧业,全面测算2000－2019年黄河流域农业水资源环境状况;基于“脱钩”的内涵,运用GIS空间分析技术探索黄河流域农业灰水足迹与经济增长关系的时空演变规律;引入无残差完全分解模型对黄河流域经济增长过程中农业灰水足迹的变化进行分解。结果 （1）总体而言,黄河流域农业灰水足迹随经济增长而增加,2013年达到极大值后缓慢下降。（2）黄河流域农业灰水足迹与经济增长的脱钩关系由无序到有序,2019年大多数地区呈强脱钩态势且存在显著的空间集聚特征。（3）2000－2019年约63%的地区在人口效应、经济效应的拉动作用下,农业灰水足迹产生增量效应;约37%的地区在城乡结构效应、技术效应和产业结构效应的抑制作用下,农业灰水足迹产生减量效应。结论 适度提高沿黄地区人口质量、控制经济发展规模以及推动绿色生产方式。     

中国水产品出口对农业经济增长的效应分析——基于1978～2007年数据的时间序列分析

本文根据1978—2007年的统计数据,对水产品出口与农业经济增长之间的动态关系进行了研究。实证研究结果表明,水产品出口与农业经济增长之间存在着一种长期稳定的均衡关系;LNRGOV（农林牧渔业总产值对数值）与LNEXAQ（水产品出口量对数值）之间存在单方面的格兰杰（Granger）因果关系;误差修正模型、脉冲响应函数和预测均方误差分解的结果表明,农业经济增长与水产品出口为正向的相关关系,但水产品出口对农业经济增长的拉动作用并不明显,而农业经济增长对水产品出口具有较明显的促进作用。     

基于混沌—神经网络时间序列的农村能源消费预测研究

农村能源是农村地区经济发展的重要基础,本文通过对农村能源消费时序的混沌辨识,指出我国农村能源消费时序是一个具有类似随机现象的混沌系统.为此,建立运用混沌神经网络时间序列的预测模型,并结合遗传算法优化神经网络权重,对我国农村能源消费进行预测得知:2010年我国农村能源消费总量为127633.07万吨标煤,预测误差较小.最后就农村能源发展提出了相关政策建议.     

深层卤水可采资源量评价的时间序列神经网络模型研究

深层地下卤水资源量的评价是国内外迄今尚未很好解决的课题,由于深部地质及水文地质参数难以准确获取,因此也难以对深层卤水资源量进行正确评价.从深层卤水开采量的时间序列出发,提出了一类基于神经网络的评价模型.首先分析了卤水开采量的时间序列特点,再建立了神经网络的拓扑结构,并设计了相应的评价算法,最后通过对某储卤构造的单井评价实例,对模型进行了验证.与传统的ARMA时间序列模型相比,其预测性能更好,对剩余可采资源量计算结果也更为准确.     

Efficient Estimation of Agricultural Time Series Models with Nonnormal Dependent Variables

This article proposes using an expanded form of the Johnson S_U family as a way to approximate nonnormal distributions in regression models. The distribution is one of the few that allows modeling heteroskedasticity and autocorrelation. The technique is evaluated with Monte Carlo simulation and illustrated through an empirical model of the West Texas cotton basis. Given nonnormality, this technique can substantially reduce the variance of slope parameter estimates relative to least squares procedures.     

中国煤炭需求预测模型研究

煤炭是中国能源的主要组成部分,对煤炭资源的消费需求的深入分析具有重要意义,这也包括对煤炭未来需求状况的预测分析.文章采集了与煤炭消费相关的产量、GDP、价格等指标及数据.利用SPSS软件采用后向消去法,求出中国煤炭消费预测模型.同时采用EXCEL软件的预测功能,求出相应的指标数值.得出2010年中国煤炭消费量,并提出相关结论.     

基于分类模型的日客流量预测

日客流量的准确预测能为景区管理决策提供科学可靠的依据。由于受到各种客观因素影响,日客流量不光呈现出强非线性特征,而且具有明显的季节性特征。整年度的日客流量数据跳跃波动过大,整年度模型很难对其进行准确预测。针对这一问题,根据黄山风景区日客流量的分布特点,建立了1、2和12月,3、6和11月,4和5月,7和8月,9和10月以及法定节假日六类预测模型,通过SVR预测方法的实证仿真显示,同年度模型相比基于客流量分布特征的分类模型明显消除了日客流量跳跃波动,有效地提高了预测精度。     

An Empirical Comparison of "Frisch" and "Time Series" Demand price Elasticities for individual Commodities

In a classical article in 1959, Ragnar Frisch [8] developed a procedure, which, under the assumption of want independence¹ and given commodity budget shares, income elasticities, and one own-price elasticity, allows one to calculate a complete matrix of own and cross price elasticities. Between broad commodity groups such an assumption (want independence) has becme increasingly accepted and in fact under the label of separability has formed the basis for a family of demand models that are increasingly used to estimate demand elasticities for broad commodity groups (the linear expenditure system, the Rotterdam model, etc.). At the individual commodity level however, the assumption of want independence seems less viable, e.g., the utility one derives from pork is in general not considered independent from one's consumption of beef. However, it has become increasingly common (and apparently acceptable) to find the Frisch methodology utilized to develop demand price elasticity estimates for individual agricultural commodities [4, 7, 17].     

Induced Innovation in United States Agriculture, 1880–1990: Time Series Tests and an Error Correction Model

An error correction model (ECM) of induced innovation, based on the two–stage CES production function allows direct tests of the inducement hypothesis, which are applied to U.S. data for 1880–1990. The time series properties of the variables include a structural break in 1920, cointegration is established and an ECM constructed, which allows factor substitution to be separated from technological change. Causality tests show that the factor–price ratios and R&D are Granger–prior to the factor–saving biases of technological change. The inducement hypothesis is corroborated, and identified as one factor in the complex development of U.S. agriculture.     

Total Response Measures in Systems of Nonlinear Equations: An Application to a Model of the U.S. Dairy Sector

This paper develops total elasticity and flexibility results for general nonlinear, dynamic systems of equations. Unlike procedures for obtaining total response measures in linear systems, analytical procedures are difficult to obtain for nonlinear models; comparable results, however, may be obtained by using numerical simulations. The potential for obtaining total elasticity measures in nonlinear dynamic models is illustrated with an annual model of the U.S. dairy sector. Results show substantial evidence of asymmetric price and quantity adjustments, as well as limit cycles. Such complexities could not be uncovered by using linear models or a partial-equilibrium framework.     

A Structural Econometric Model of Joint Consumption of Goods and Recreational Time: An Application to Pick-Your-Own Fruit

This article develops a structural econometric consumer demand model for goods, which have time and monetary costs, and where time spent obtaining the goods also enters into the utility function. The model is used to analyze customers' decision to buy pick-your-own versus preharvested strawberries at North Carolina pick-your-own fruit operations. The analysis distinguishes the effect of time as a resource constraint and time that provides utility. Demand for strawberries sold at the operations is price elastic, and demand for pick-your-own strawberries is less price elastic than demand for preharvested strawberries.