郑州市机动车新车入户量预测研究

对郑州市2009～2012年机动车新车入户量的数据进行一阶弱化算子处理,然后建立新车入户量的灰色模型GM(1,1),并预测了未来5年郑州市机动车新车入户量.     

《市场调查和预测》中直线方程的系数求解方法探讨

《市场调查和预测》是市场营销专业和工商管理专业的一门必修课程,市场预测的时间序列法和因果分析法中经常会提到直线方程,关于直线方程的系数求解,不同情况下可以用不同的方法解决,为了帮助学生理解记忆、提高教学效果,现专门针对直线方程的系数求解及应用范围进行例题讲解并归纳总结．     

灰色多变量模型在运动员竞技能力发展预测中的应用研究

从探讨运动员竞技能力系统发展的一般特征出发,结合实例介绍灰色多变量预测模型在运动员竞技能力系统发展中的建模、预测方法,并通过与单因素模型的实际对比,阐述灰色多变量预测模型的实用价值和应用前提,为运动训练及其他体育领域的灰色预测提供了一种方法与思路。     

关于"计算机未来学"学科理论的思索

计算机未来学是关于计算机科学、技术及其产品的未来发展趋势的学科.开展"计算机未来学"新学科研究,对计算机科学的发展进行宏观研究,从整体上、宏观上把握计算机科学的发展趋势,这对制定正确的计算机科学发展策略,调整研究起点和开发方向,有着指导性的意义.     

A study on forecasting electricity production and consumption in smart cities and factories

The electrical power sector must undergo a thorough metamorphosis to achieve the ambitious targets in greenhouse gas reduction set forth in the Paris Agreement of 2015. Reducing uncertainty about demand and, in case of renewable electricity generation, supply is important for the determination of spot electricity prices. In this work we propose and evaluate a context-based technique to anticipate the electricity production and consumption in buildings. We focus on a household with photovoltaics and energy storage system. We analyze the efficiency of Markov chains, stride predictors and also their combination into a hybrid predictor in modelling the evolution of electricity production and consumption. All these methods anticipate electric power based on previous values. The main goal is to determine the best method and its optimal configuration which can be integrated into a (possibly hardware-based) intelligent energy management system. The role of such a system is to adjust and synchronize through prediction the electricity consumption and production in order to increase self-consumption, reducing thus the pressure over the power grid. The experiments performed on datasets collected from a real system show that the best evaluated predictor is the Markov chain configured with an electric power history of 100 values, a context of one electric power value and the interval size of 1.     

2019年夏季全国气候趋势展望

2019年春夏季,赤道中东太平洋将维持弱-中等强度厄尔尼诺（El Niño）。中国科学院大气物理研究所的全国气候趋势预测结果表明,预计2019年汛期（6—8月）,长江中下游以南大部分地区降水偏多,其中江南地区偏多2—5成,可能发生局地洪涝灾害;新疆北部、东北北部、四川东部和陕西南部等地降水正常略偏多。我国其他大部分地区降水正常略偏少,其中河套地区降水偏少2—5成。预计2019年登陆台风数正常略偏多。     

新能源汽车产业与美丽中国建设的耦合协调及前景预测研究

美丽中国是一个以生态文明建设为核心,经济、政治、文化与社会和谐发展的复杂巨系统。发展新能源汽车产业不仅可以破解环境污染和能源短缺的双重危机,而且能够培育新的经济增长点,与美丽中国建设具有天然的耦合性。通过文献研究与专家咨询,选取新能源汽车产业发展和美丽中国建设综合评价指标体系,在此基础上,采用改进的熵值法,构建耦合协调度模型和BP神经网络预测模型,对动态耦合问题进行定量研究。研究发现：(1)新能源汽车产业系统和美丽中国系统的综合发展水平均呈现稳定上升态势,整体而言新能源汽车产业发展相对滞后;(2)新能源汽车产业-美丽中国系统耦合度始终处于由拮抗向磨合过渡的阶段,但两大系统的相互联系和相互促进作用逐渐加强,耦合协调效益正在逐步提升;(3)预测结果表明未来5年新能源汽车产业系统与美丽中国系统协同发展功效持续增强,耦合协调度将从中级协调跨入良好协调阶段。     

基于因子分析和曲线拟合的集装箱吞吐量预测

为提高集装箱吞吐量的预测精度,提出基于因子分析和曲线拟合的集装箱吞吐量预测模型。以上海港为例,通过因子分析,分析影响集装箱吞吐量的主要因素,筛选出主因子,得到不同年份的综合经济发展值;再运用曲线拟合方法,建立以综合经济发展值为自变量,以集装箱吞吐量为因变量的三次曲线模型;运用自回归积分移动平均(autoregressive integrated moving average,ARIMA)模型预测2016—2020年的综合经济发展值,进而求得2016—2020年上海港集装箱吞吐量预测值。结果表明:该模型的拟合效果和预测精度均较高,可以运用到集装箱吞吐量预测中。给出上海港在国内经济新常态下转型升级的建议。     

灰色预测法在情报研究中的应用研究综述

[目的/意义] 灰色预测法可有效处理情报研究中广泛存在的小样本数据，通过对灰色预测法在情报研究中的应用情况进行梳理，总结其在应用过程中存在的不足，为灰色预测法在情报研究中的进一步应用提供参考。[方法/过程] 通过综述情报研究中涉及灰色预测法的相关文献，从数据选取、模型构建和解决的问题等方面对情报研究中灰色预测法的应用进行概述，总结当前情报研究中灰色预测法的应用所存在的问题，并提出改进建议。[结果/结论] 在方法应用上，已有研究主要采用数列灰预测，且模型集中在单变量灰色预测模型，根据预测对象不同，灰色预测法已经在包括期刊分析、图书馆运行管理、热点主题分析及科研机构评价方面得到了很好的应用，未来可根据预测对象特点及研究目标尝试不同的灰色预测方法，扩宽灰色预测法在其他方面的情报研究问题中的应用。     

Location extraction from tweets

Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. These events are represented by three main dimensions: time, location and entity-related information. The focus of this paper is location, which is an essential dimension for geo-spatial applications, either when helping rescue operations during a disaster or when used for contextual recommendations. While the first type of application needs high recall, the second is more precision-oriented. This paper studies the recall/precision trade-off, combining different methods to extract locations. In the context of short posts, applying tools that have been developed for natural language is not sufficient given the nature of tweets which are generally too short to be linguistically correct. Also bearing in mind the high number of posts that need to be handled, we hypothesize that predicting whether a post contains a location or not could make the location extractors more focused and thus more effective. We introduce a model to predict whether a tweet contains a location or not and show that location prediction is a useful pre-processing step for location extraction. We define a number of new tweet features and we conduct an intensive evaluation. Our findings are that (1) combining existing location extraction tools is effective for precision-oriented or recall-oriented results, (2) enriching tweet representation is effective for predicting whether a tweet contains a location or not, (3) words appearing in a geography gazetteer and the occurrence of a preposition just before a proper noun are the two most important features for predicting the occurrence of a location in tweets, and (4) the accuracy of location extraction improves when it is possible to predict that there is a location in a tweet.