智慧金融——Work Smarter

围绕金融业和信息产业的发展阶段的论述,通过银行业和信息系统最佳实践的角度,探索利用新思维和新技术构建＂智慧金融＂体系的最基本要素。以SEB银行为便,解析商业银行利用新技术和商业智能功能获取更多信息和洞察力,帮助客户利用最新电子交易渠道和电子服务产品。金融智能化必将是现代金融学理论、金融业界经验、科技发展与运用、社会进步与生活智能化的集大成者。     

Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction

This paper reports the results of the NN3 competition, which is a replication of the M3 competition with an extension of the competition towards neural network (NN) and computational intelligence (CI) methods, in order to assess what progress has been made in the 10 years since the M3 competition. Two masked subsets of the M3 monthly industry data, containing 111 and 11 empirical time series respectively, were chosen, controlling for multiple data conditions of time series length (short/long), data patterns (seasonal/non-seasonal) and forecasting horizons (short/medium/long). The relative forecasting accuracy was assessed using the metrics from the M3, together with later extensions of scaled measures, and non-parametric statistical tests. The NN3 competition attracted 59 submissions from NN, CI and statistics, making it the largest CI competition on time series data. Its main findings include: (a) only one NN outperformed the damped trend using the sMAPE, but more contenders outperformed the AutomatANN of the M3; (b) ensembles of CI approaches performed very well, better than combinations of statistical methods; (c) a novel, complex statistical method outperformed all statistical and CI benchmarks; and (d) for the most difficult subset of short and seasonal series, a methodology employing echo state neural networks outperformed all others. The NN3 results highlight the ability of NN to handle complex data, including short and seasonal time series, beyond prior expectations, and thus identify multiple avenues for future research.     

Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

In this work we introduce the forecasting model with which we participated in the NN5 forecasting competition (the forecasting of 111 time series representing daily cash withdrawal amounts at ATM machines). The main idea of this model is to utilize the concept of forecast combination, which has proven to be an effective methodology in the forecasting literature. In the proposed system we attempted to follow a principled approach, and make use of some of the guidelines and concepts that are known in the forecasting literature to lead to superior performance. For example, we considered various previous comparison studies and time series competitions as guidance in determining which individual forecasting models to test (for possible inclusion in the forecast combination system). The final model ended up consisting of neural networks, Gaussian process regression, and linear models, combined by simple average. We also paid extra attention to the seasonality aspect, decomposing the seasonality into weekly (which is the strongest one), day of the month, and month of the year seasonality.     

竞争情报与企业的蓬勃发展

在信息高度发达的时代,企业的的发展不仅是经济实力的发展,更重要的是情报的竞争,建立企业情报竞争系统,成立竞争情报服务中心,对提高企业发展,有着至关重要的作用。     

基于“会诊式”推理控制策略的智能型搜索引擎的研究

随着Internet和网络技术的发展,搜索引擎给人们带来的很大便利。本文分析了传统搜索引擎的缺点,探讨了"会诊式"推理控制策略的结构和特点,研究了基于"会诊式"推理控制策略的智能型搜索引擎的特征,并给出其实现技术。     

浅谈知识型员工的激励机制

在知识经济时代,如何激励知识型员工,知识型员工如何实现自己的价值已成为亟需研究的课题。文章试从知识型员工的含义和特点出发,阐释了如何构建知识型员工的激励制度,以达到激励知识型员工的目的。     

试谈离散数学在计算机学科中的重要性

阐述了离散数学及计算机专业其他学科的基本概念，分析了离散数学与计算机专业其他学科间的关系。教师在教学过程中，将此关系在课堂中灌输给学生，让学生了解离散数学在计算机专业学科中所处的重要地位。学生在学习离散数学的同时，能明确该学科的学习目的，也能站在较高的层次把握好离散数学的学习。     

我国家族型民营企业可持续发展的探讨

产权的单一性和封闭性、家族式的亲情性管理 ,使得我国家族型民营企业不能形成一种开放式的系统组织结构 ,使得它们不能有效地吸引和吸收外部的智力资本和金融资本 ,严重制约了这类企业的可持续发展。我国家族型民营企业可持续发展 ,必须采取引入多元化投资主体 ,引入职业经理人 ,向专业化管理过渡等对策     

支持物流系统的智能化方向

介绍了ＣＡＬＳ在物流领域的应用前景，论述了物流技术发展及其智能化方向的理想，概念等有关问题，最后介绍了假想物流系统技术的概念和发展及其应用的意义。     

数据科技：智慧金融的技术基础

数据科技是开发网络空间（CYBER SPACE）数据资源所用到的科技,是发展包括智慧金融在内的智慧产业的技术基础。本文介绍了数据科技和数据产业的内涵,探讨了数据在各类智慧系统中的核心作用,以数据科技为技术基础的智慧金融,提出了发展金融数据共享规范、金融数据产品、绿色存储、高可信环境等建议,通过发展数据科技推进智慧金融的进步。