马尔可夫链模型在灾变预测中的应用

利用马尔可夫链模型的原理预测灾变，以郑州市旱涝等级的预测作为实例，介绍了使用这种模型的方法与步骤，预测结果表明，利用马尔可夫链模型预测灾变是可行的。     

Rainfall forecasting by technological machine learning models

Accurate forecasting of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. Recurrent artificial neural networks (RNNS) have played a crucial role in forecasting rainfall data. Meanwhile, support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. This investigation elucidates the feasibility of hybrid model of RNNs and SVMs, namely RSVR, to forecast rainfall depth values. Moreover, chaotic particle swarm optimization algorithm (CPSO) is employed to choose the parameters of a SVR model. Subsequently, example of rainfall values during typhoon periods from Northern Taiwan is used to illustrate the proposed RSVRCPSO model. The empirical results reveal that the proposed model yields well forecasting performance, RSVRCPSO model provides a promising alternative for forecasting rainfall values.     

ANN技术在企业经济预测与预警中的应用研究

提出了适合于上市公司而建立的基于ANN技术的企业经济综合指标短期预警系统的构建方案.该系统是一个人机相结合的反馈式预警系统,包括危机判定、财务指标预测、预警知识获取和报警四个子系统.该系统将定性分析与定量分析结合起来,既突出人的作用,又充分发挥了人工神经网络的在预测方面的技术,使得两者有机的结合在一起.其中,预警指标预测子系统体现了ANN技术在时间序列预测方面的应用,而知识获取子系统和报警子系统则体现了ANN技术在回归预测方面的应用,两者都有很好的理论基础.同时,该系统建立的程序比较规范,具有普适性,易于操作,比较容易实现.最后,还对该系统进行了实证模拟分析,并与专家意见结果进行了对比,验证了其有效性.     

灰色预测模型的再拓广(英文)

本文通过用对数函数对原始数据列｛ｘ（０）（ｋ）｝进行预测，又提出用幂函数对原始数据列进行变换，以提高离散数据的光滑度。在此基础上再将对数变换与幂函数变换两者结合起来，产生一种新的变换——“对数函数—幂函数变换”，达到了进一步增加数据的光滑度，提高预测精度的目的。     

税源预测的数学模型

税源预测是财税部门的重要工作。本文通过实例提出税源预测中常用的折扣最小二乘法、一元线性回归、税收弹性及马尔柯夫预测方法等数学模型，使预测税源工作定量化、科学化。     

Conjugate processes: Theory and application to risk forecasting

Many dynamical phenomena display a cyclic behavior, in the sense that time can be partitioned into units within which distributional aspects of a process are homogeneous. In this paper, we introduce a class of models – called conjugate processes – allowing the sequence of marginal distributions of a cyclic, continuous-time process to evolve stochastically in time. The connection between the two processes is given by a fundamental compatibility equation. Key results include Laws of Large Numbers in the presented framework. We provide a constructive example which illustrates the theory, and give a statistical implementation to risk forecasting in financial data.     

The impact of special days in call arrivals forecasting: A neural network approach to modelling special days

A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established, statistical methods, we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter, we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe’s leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods.     

基于EMD-GA-BP与EMD-PSO-LSSVM的中国碳市场价格预测

由于碳交易市场价格的波动性大及相互影响关系的复杂性,本文试图构建碳价格长期和短期的最优预测模型。考虑到碳交易价格波动的趋势性和周期性特点,基于经验模态分解算法(EMD)、遗传算法(GA)—神经网络(BP)模型、粒子群算法(PSO)—最小二乘支持向量机(LSSVM)模型及由它们构建的组合预测模型,对中国碳市场交易价格进行短期预测和长期预测。实证分析中将影响碳交易价格的不同宏观经济因素和碳价格时间序列因素做为输入变量,分别代入组合模型进行预测。研究结果表明,在短期预测中,EMD-GA-BP模型预测效果优于GA-BP模型和PSO-LSSVM模型;而在长期预测中,组合模型EMD-PSO-LSSVM模型预测效果优于只考虑碳价格波动趋势性或周期性预测效果。     

Fuzzy adaptive decision-making for boundedly rational traders in speculative stock markets

The development of new models that would enhance predictability for time series with dynamic time-varying, nonlinear features is a major challenge for speculators. Boundedly rational investors called “chartists” use advanced heuristics and rules-of-thumb to make profit by trading, or even hedge against potential market risks. This paper introduces a hybrid neurofuzzy system for decision-making and trading under uncertainty. The efficiency of a technical trading strategy based on the neurofuzzy model is investigated, in order to predict the direction of the market for 10 of the most prominent stock indices of U.S.A, Europe and Southeast Asia. It is demonstrated via an extensive empirical analysis that the neurofuzzy model allows technical analysts to earn significantly higher returns by providing valid information for a potential turning point on the next trading day. The total profit of the proposed neurofuzzy model, including transaction costs, is consistently superior to a recurrent neural network and a Buy & Hold strategy for all indices, particularly for the highly speculative, emerging Southeast Asian markets. Optimal prediction is based on the dynamic update and adaptive calibration of the heuristic fuzzy learning rules, which reflect the psychological and behavioral patterns of the traders.     

组合预测方法在旅游经济分析预测中的应用

应用组合预测方法,对浙江省旅游业主要指标的统计数据建立数学模型,对旅游经济发展进行现状分析和前景预测,并探讨了旅游经济对社会经济发展的影响、研究表明所建模型具有很好的拟合精度,较好的刻画了浙江省旅游经济的发展过程,这将为研究和调控旅游经济发展趋势提供有益的参考依据.