An approach to discovering multi-temporal patterns and its application to financial databases

Managerial decision-making processes often involve data of the time nature and need to understand complex temporal associations among events. Extending classical association rule mining approaches in consideration of time in order to obtain temporal information/knowledge is deemed important for decision support, which is nowadays one of the key issues in business intelligence. This paper presents the notion of multi-temporal patterns with four different temporal predicates, namely before, during, equal and overlap, and discusses a number of related properties, based on which a mining algorithm is designed. This enables us to effectively discover multi-temporal patterns in large-scale temporal databases by reducing the database scan in the generation of candidate patterns. The proposed approach is then applied to stock markets, aimed at exploring possible associative movements between the stock markets of Chinese mainland and Hong Kong so as to provide helpful knowledge for investment decisions.     

小议廊坊市财政网站建设和管理成效

政府门户网站是地方各级政府单位通过互联网方式对外展示政府形象,实现政府信息对外公开的重要渠道。财政部门作为政府的一个重要的综合经济管理部门,其网站建设与管理在财政工作中发挥着重要的作用。该文以廊坊市财政网站从建设到管理实践过程为例,系统论述了财政网站建设的重要性,并详细总结了该网站建成后的一系列成效。     

线性控制系统多面体区域的生存性判别

利用非光滑分析, 讨论线性控制系统多面体区域的生存性判别. 对于有界多面体(利用有限点集的凸包来表示), 其生存性判别只需检验其在极点处是否满足生存性条件, 去掉了以往对输入集合为多面体的要求, 这种生存性判别方法简便易行. 最后利用所给出的生存性条件讨论了生存性设计.

     

Creating the Enabling Environment for More Transparent and Better-resourced Local Governments: A Case of E-taxation in the Philippines

This research joins the growing body of literature that advocates for the use of information and communication technology (ICT) in local governance more particularly in public financial management. Using a case study in Bohol, a province in the Philippines, this paper discusses the impact of ICT on local revenue generation by analyzing both quantitative and qualitative data from 15 municipalities which used e-taxation. This paper argues that the use of ICT can make possible more transparent and accountable revenue generation systems to benefit both government and taxpayers. However, these results are differentiated depending on the level of political leadership, the nature of articulation of the demand for ICT use, the ratio of benefit against cost, and the availability of technical skills and resources at the sub-national level. It is within this context that an eco-system analysis is argued to be useful in analyzing how ICT can be adopted, scaled, and used by sub-national governments to achieve better governance.     

Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion

Financial time series prediction is regarded as one of the most challenging job because of its inherent complexity, and the hybrid forecasting model incorporating autoregressive integrated moving average and support vector machine (SVM) has been implemented widely to deal with the both linear and nonlinear patterns in time series data. However, the SVM model does not take into consideration the time correlation knowledge between different data points in time series, which impacts the learning efficiency of the SVM in real application. To overcome this restriction, this paper proposes the Taylor Expansion Forecasting model as an alternative to the SVM and develops a novel hybrid methodology via combining autoregressive integrated moving average and Taylor Expansion Forecasting to exploit the comprehensive forecasting capacity to the financial time series data with noise. Both theoretical proof and empirical results obtained on several commodity future prices demonstrate that the proposed hybrid model improves greatly the forecasting accuracy.     

面向大规模金融对账文件的近似比对模型及系统

针对TB级的大规模金融对账文件的近似比对问题,本文深入分析了金融对账文件的特点,以提升比对速度作为研究目标,提出了一种多层次的近似比对模型-UpCompare模型.UpCompare模型以多进程为扩展基础,采用哈希索引建立映射表结合快速致胜策略为核心算法.测试结果表明,运用UpCompare模型,我国银行卡清算系统的每日清算文件近似比对效率提升了5倍以上.     

财务报销单据智能审批系统

随着市场竞争日趋激烈,成本控制对企业的重要性日益增加.财务报销系统是企业成本支出的重要窗口,对控制企业成本起着关键性的作用.传统的财务报销管理软件侧重于业务处理,对报销支出的管控较弱,而且各级管理者在审批报销单据时常常因为缺乏相关信息而只能走过场式审批.本文目标是通过构建财务报销单据智能审批系统,为审批人提供审批决策支持,从而提高审批效率和审批质量,降低企业成本.本文对财务报销单据智能审批系统的基本功能和应用流程进行了详细介绍.     

Feedforward networks in financial predictions: the future that modifies the present

The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the 'self-momentum', 'Freud' and 'Jung' rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated 'learning to learn' characteristics are now under study and test.     

智能财务分析与决策支持软件的现状与发展

财务软件在经历多年发展之后,决策支持软件（DSS）的引入使财务软件应用达到了一个新的高度。本文通过分析决策支持软件（DSS）的现状与发展,并以实际的软件对比阐述了智能财务软件的发展方向。     

Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference

Financial forecasting is an example of a signal processing problem which is challenging due to small sample sizes, high noise, non-stationarity, and non-linearity. Neural networks have been very successful in a number of signal processing applications. We discuss fundamental limitations and inherent difficulties when using neural networks for the processing of high noise, small sample size signals. We introduce a new intelligent signal processing method which addresses the difficulties. The method proposed uses conversion into a symbolic representation with a self-organizing map, and grammatical inference with recurrent neural networks. We apply the method to the prediction of daily foreign exchange rates, addressing difficulties with non-stationarity, overfitting, and unequal a priori class probabilities, and we find significant predictability in comprehensive experiments covering 5 different foreign exchange rates. The method correctly predicts the directionof change for the next day with an error rate of 47.1%. The error rate reduces to around 40% when rejecting examples where the system has low confidence in its prediction. We show that the symbolic representation aids the extraction of symbolic knowledge from the trained recurrent neural networks in the form of deterministic finite state automata. These automata explain the operation of the system and are often relatively simple. Automata rules related to well known behavior such as tr end following and mean reversal are extracted.