邯郸国际物流园区物流量预测

提出将灰色理论与马尔柯夫预测相结合,运用到邯郸国际物流园区物流量的预测,分析结果表明,基于灰色马尔柯夫的预测模型精度高于灰色预测模型,同时弥补了单-马尔柯夫链预测依赖于系统分布参数的局限,为邯郸国际物流园区的建设规模提供了参考的依据.     

试析黄金市场的灰色——马尔可夫预测

本文通过对影响黄金市场的因素的分析,利用灰色系统理论建立相应的灰色——马尔柯夫预测模型,并结合对上海黄金交易所AU99.95价格的预测说明该模型具有较好的预报精度和应用价值。     

灰色-马尔柯夫预测模型在物流产品回收中的应用研究

针对逆向物流预测中不稳定性因素进行定量和定性分析,制定出相应的改善方法,让逆向物流量趋向稳定,提高工作效率。最后,针对逆向物流的要求对马尔柯夫预测和灰色预测进行改进,吸取两者的优点,建立灰色-马尔柯夫预测模式,促进逆向物流预测工作的顺利开展。     

灰色-马尔柯夫预测模型在物流产品回收中的应用研究

针对逆向物流预测中不稳定性因素进行定量和定性分析,制定出相应的改善方法,让逆向物流量趋向稳定,提高工作效率.最后,针对逆向物流的要求对马尔柯夫预测和灰色预测进行改进,吸取两者的优点,建立灰色-马尔柯夫预测模式,促进逆向物流预测工作的顺利开展.     

马尔柯夫法在石油价格预测中的应用

21世纪世界对能源的争夺将越来越激烈。各国为保障本国的经济稳定持续发展,都不遗余力地加入到对能源,特别是石油的争夺中来。本文通过对近四年世界石油价格的分析,利用马尔柯夫预测法,对未来油价的高低可能性进行预测,对马尔柯夫预测法的适用性进行总结。     

基于灰色——马尔柯夫模型的逆向物流量预测

针对逆向物流量不稳定、波动性较大的特点,对不满足惯性要求的原始数据进行数据变换处理后,综合灰色GM（1,1）模型和马尔柯夫模型各自的优势,建立相应的逆向物流量预测模型,对某一汽车零部件企业的逆向物流量进行预测。结果表明该模型预测精度较高,具有一定应用价值。     

基于马尔柯夫预测模型的智能停车有效泊位预测研究

随着绍兴城市化水平的不断提升与城镇居民私家车拥有量的迅速增加,有限的停车位无法有效解决停车的问题显得越来越突出。通过调研,确定以绍兴市世茂广场停车场作为研究对象,基于马尔柯夫预测模型进行短时有效泊位的验证分析。通过计算,运用马尔柯夫预测模型所得的预测结果可信度较高,可在智能停车诱导系统模块中加以使用。     

灰色马尔可夫模型在广东旅游人数预测中的应用

基于广东2005～2011年广东入境旅游人数数据,采用灰色GM(1,1)预测模型动态模拟广东入境旅游人数变化态势,并运用马尔柯夫状态转移矩阵对灰色GM(1,1)模型的模拟结果进行修正,以提高预测精度。     

马尔柯夫链在高速铁路交通量预测中的应用

在一定时期内,两地间总的交通量变化是不大的,但各种交通方式所承担的交通量却在动态地发生着变化,应用马尔柯夫链理论,研究和分析这种交通量的变化规律,用高速铁路交通量在这一过程中的变化来说明问题,并由此预测出未来一段时间内的变化趋势.     

基于BP神经网络模型的国内旅游人数预测

旅游人数的分析和预测是旅游规划与管理的关键性、基础性工作。目前旅游人数预测主要采用基于传统研究方法的预测方法。提出了一种基于BP神经网络模型的国内旅游人数预测新方法,对国内旅游人数的变化趋势进行了综合分析与预测,结果表明该方法具有较高的精度,该模型在旅游人数预测中的应用是可行的。     

Latent Markov Modelling of Recidivism Data

This article discusses the application of latent Markov modelling for the analysis of recidivism data. We briefly examine the relations of Markov modelling with log–linear analysis, pointing out pertinent differences as well. We show how the restrictive Markov model may be more easily applicable by adding latent variables to the model, in which case the latent Markov model is a dynamic version of the latent class model. As an illustration, we apply latent Markov analysis on an empirical data set of juvenile prosecution careers, showing how the Markov analyses producing well-fitting and interpretable solutions. We end by comparing the possible contributions of Markov modelling in recidivism research, outlining its drawbacks as well. Recommendations and directions for future research conclude the article.     

The application of a Markov Chain in educational planning

Mathematical models can assist educators in the preparation of their educational plans and their potential in this regard is being increasingly realized. As a result, models have found application at all levels at which planning is conducted.This paper examines the potential of one model—the Markov Chain—which is capable of predicting enrolments for an education system.The model is applied to the New South Wales State Government education system between 1947 and 1961 and the projected enrolments compared to the actual enrolments in those years.Some success is achieved but it appears as if the data rather than the model are responsible for this. The limitations of the Markov Chain approach are discussed and present research and directions listed.     

Validating an agent-based model of the Zipf׳s Law: A discrete Markov-chain approach

This study discusses the validation of an agent-based model of emergent city systems with heterogeneous agents. To this end, it proposes a simplified version of the original agent-based model and subjects it to mathematical analysis. The proposed model is transformed into an analytically tractable discrete Markov model, and its city size distribution is examined. Its discrete nature allows the Markov model to be used to validate the algorithms of computational agent-based models. We show that the Markov chains lead to a power-law distribution when the ranges of migration options are randomly distributed across the agent population. We also identify sufficient conditions under which the Markov chains produce the Zipf׳s Law, which has never been done within a discrete framework. The conditions under which our simplified model yields the Zipf׳s Law are in agreement with, and thus validate, the configurations of the original heterogeneous agent-based model.     

Estimation of Markov regime-switching regression models with endogenous switching

Following Hamilton [1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357–384], estimation of Markov regime-switching regressions typically relies on the assumption that the latent state variable controlling regime change is exogenous. We relax this assumption and develop a parsimonious model of endogenous Markov regime-switching. Inference via maximum likelihood estimation is possible with relatively minor modifications to existing recursive filters. The model nests the exogenous switching model, yielding straightforward tests for endogeneity. In Monte Carlo experiments, maximum likelihood estimates of the endogenous switching model parameters were quite accurate, even in the presence of certain model misspecifications. As an application, we extend the volatility feedback model of equity returns given in Turner et al. [1989. A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics 25, 3–22] to allow for endogenous switching.     

Probabilistic forecasting of industrial electricity load with regime switching behavior

This paper suggests a novel inhomogeneous Markov switching approach for the probabilistic forecasting of industrial companies’ electricity loads, for which the load switches at random times between production and standby regimes. The model that we propose describes the transitions between the regimes using a hidden Markov chain with time-varying transition probabilities that depend on calendar variables. We model the demand during the production regime using an autoregressive moving-average (ARMA) process with seasonal patterns, whereas we use a much simpler model for the standby regime in order to reduce the complexity. The maximum likelihood estimation of the parameters is implemented using a differential evolution algorithm. Using the continuous ranked probability score (CRPS) to evaluate the goodness-of-fit of our model for probabilistic forecasting, it is shown that this model often outperforms classical additive time series models, as well as homogeneous Markov switching models. We also propose a simple procedure for classifying load profiles into those with and without regime-switching behaviors.     

模糊聚类的马尔可夫方法及神经网络法在铁矿石需求预测中的应用

以徐州市生铁产量及其与铁矿石需求量之间的关系为基础,通过模糊聚类的马尔可夫方法、神经网络法进行预测,发现两种方法所得结果基本吻合,旨在为决策者提供参考依据。     

基于GSPN的装备器材供应链建模与分析

研究了广义随机Petri网（GSPN）的建模及分析方法,建立了基于GSPN的装备器材供应链流程模型,并将Petri网模型转化为等价的马尔可夫链,得出了供应链模型的主要性能指标,据此分析制约供应链的瓶颈。     

Testing the unbiased forward exchange rate hypothesis using a Markov switching model and instrumental variables

This paper develops a model for the forward and spot exchange rate which allows for the presence of a Markov switching risk premium in the forward market and considers the issue of testing the unbiased forward exchange rate (UFER) hypothesis. Using US/UK data, it is shown that the UFER hypothesis cannot be rejected, provided that instrumental variables are used to account for within‐regime correlation between explanatory variables and disturbances in the Markov switching model on which the test is based. Copyright © 2005 John Wiley & Sons, Ltd.     

浅谈计算机在森林病虫害预测预报中的应用——以马尔可夫预测模型为例

提高病虫预测预报的准确性和时效性,对森林资源的保护和生态环境的建设具有重大的意义,将计算机应用在森林病虫害预测预报中,对于我国林业发展起着重要作用。本文主要介绍了通过计算机在森林病虫害预测预报中的应用,通过马尔科夫模型,实现了病虫害的预测预报。提高病虫预测预报的准确性和时效性,对森林资源的保护和生态环境的建设具有重大的意义。     

MODELLING REGIME SWITCHING AND STRUCTURAL BREAKS WITH AN INFINITE HIDDEN MARKOV MODEL

This paper proposes an infinite hidden Markov model to integrate the regime switching and structural break dynamics in a unified Bayesian framework. Two parallel hierarchical structures, one governing the transition probabilities and another governing the parameters of the conditional data density, keep the model parsimonious and improve forecasts. This flexible approach allows for regime persistence and estimates the number of states automatically. An application to US real interest rates compares the new model to existing parametric alternatives. Copyright © 2013 John Wiley & Sons, Ltd.