基于偏离度分析法的江苏省旅游就业潜力预测

当前，旅游业的就业功能受到广泛关注和普遍重视，预测和把握旅游就业潜力对促进一个国家和地区旅游业的发展乃至国民经济的发展意义重大。在借鉴已有相关研究成果的基础上，以江苏省为例，运用偏离度分析法对其旅游就业潜力进行了具体测算，以期对江苏省旅游就业工作的发展有所裨益。     

城市货运发展统计数据分析与建模

21世纪的物流在上海的经济活动中扮演着重要的角色,货运在城市交通中所占的比重日益增 加,如何科学地预测货运总量的发展趋势,对上海城市交通规划有着重要的意义。本文采用基于时间序列的 ARIMA模型,构建了上海货运总量发展模型,并对上海中短期的货运发展情况进行预测。     

随机模糊神经网络模型预测铁水硅含量

该文在考虑了具有模糊化和非模糊化的模糊逻辑系统用于高炉铁水含硅量[Si]时各种噪声干扰的同时,把模糊数学理论和随机系统理论结合在一起,建立了一种新的高炉铁水含硅量[Si]的智能预报模型,该模型是由非单值模糊化、模糊规则库、模糊推理机、特殊非模糊化构成的随机模糊神经网络逻辑系统.通过结合莱钢1号高炉实时在线采集的生产数据,进行仿真研究,结果表明,该套模型能够很好地预测高炉铁水含硅量[Si].     

Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model

The study of climatic variables that govern the Indian summer monsoon has been widely explored. In this work, we use a non-linear deep learning-based feature reduction scheme for the discovery of skilful predictors for monsoon rainfall with climatic variables from various regions of the globe. We use a stacked autoencoder network along with two advanced machine learning techniques to forecast the Indian summer monsoon. We show that the predictors such as the sea surface temperature and zonal wind can predict the Indian summer monsoon one month ahead, whereas the sea level pressure can predict ten months before the season. Further, we also show that the predictors derived from a combination of climatic variables can outperform the predictors derived from an individual variable. The stacked autoencoder model with combined predictors of sea surface temperature and sea level pressure can predict the monsoon (June-September) two months ahead with a 2.8% error. The accuracy of the identified predictors is found to be superior to the state-of-the-art predictions of the Indian monsoon.     

区域物流需求量预测分析

区域物流需求量预测是政府有关部门制定物流规划和建设物流基地的决策依据，也是第三方物流企业发展物流业务的重要前提。本文在对区域需求量变化的影响因素分析以及对区域物流需求量指标选取的基础上．采用灰色预测方法对区域物流需求量进行预测。     

战役后勤仓库军用集装箱装卸机械优化配置研究

根据战役后勤仓库军用集装箱、装卸的特点,建立了某型集装箱装卸机械优化配置的仿真模型,并对其进行了实例分析。     

An effective going concern prediction model for the sustainability of enterprises and capital market development

The purpose of this study is to construct a two-stage effective and innovative going concern prediction model to predict going concern doubt for the sustainability of enterprises and capital market development. Samples of this study are the companies listed on the Taiwan Stock Exchange or the Taipei Exchange, totalling 196 companies and including 49 companies with going concern doubt and 147 normal companies (with no going concern doubt). The data are taken from the Taiwan Economic Journal (TEJ) and the Market Observation Post System during the period from 2001 to 2016 (totalling 16 years). This study adopts a two-stage way to construct the going concern prediction models. In Stage I, the traditional statistical method of stepwise regression (SR) and the data mining technique artificial neural network (ANN) are applied to select the important variables. In Stage II, two decision tree algorithms (data mining techniques): classification and regression tree (CART) and C5.0 are used to establish the prediction models. The results show that the SR + CART model has the highest going concern prediction accuracy, with an overall accuracy of 87.42%.     

灰色预测算法在物流园区需求预测中的应用

对物流园区需求预测进行了系统分析,介绍了灰色预测算法,和物流园区腹地划分方法;结合连云港港口物流园区需求预测的实例进一步阐述了灰色预测算法在物流园区需求预测中的应用.     

油气勘探目标的经济评价

勘探目标经济评价要坚持常规经济评价和风险评价相结合，在用多种方法合理预测经济评价参数，充分考虑各种风险因素的基础上，认真做好风险评价研究，提高勘探目标经济评价的可靠性。     

商业银行信用风险评估的生存分析模型及实证研究

企业发生财务危机，不能归还到期贷款是商业银行信贷资产的主要风险来源，商业银行如何构建恰当的信用风险评估模型来预测企业的财务危机，从而避免这类信用风险的出现就显得尤为重要。本文以我国上市公司为研究对象，结合杜邦分析法建立了基于生存分析的信用风险评估模型，模型对于随机选取的预测样本，其提前1年、2年和3年的预测准确率分别达到86％、72％和68％。通过与Ahman模型、Ohlson模型预测结果的比较和鲁棒性检验的结果发现，该模型同时具有可以使用时间序列、无需样本配对、中远期预测能力强和高鲁棒性的特点．这些特点特别对于商业银行中长期信贷风险管理具有较高的应用价值．