共查询到19条相似文献,搜索用时 203 毫秒
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贝叶斯网络是上世纪80年代发展起来的一种概率图形模型,它提供了不确定性环境下的知识表示、推理、学习手段,可以完成决策、诊断、预测、分类等任务,已广泛应用于数据挖掘、语音识别、工业控制、经济预测、医疗诊断等诸多领域。然而由于贝叶斯网络的推理和贝叶斯网络的学习问题都是NP难的,其实际应用受到很大限制。贝叶斯网络推理是利用它进行决策、诊断、分类、预测等应用的基础,其本质任务是计算边缘概率分布。当网络比较复杂时,推理将变得不可行。多模块的贝叶斯网络(MSBN)从简化模型本身出发,对贝叶斯网络进行了扩展。我们则提出了一种用于MSBN中的近似推理算法,这些都大大拓宽了贝叶斯网络的应用领域。 相似文献
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研究变量之间的预测能力在许多领域都有重要意义,通过这种研究,能够揭示变量之间的制约机制,贝叶斯网络是研究变量之间预测能力的有力工具.本文使用依赖分析方法建立基于贝叶斯网络的马尔科夫毯预测,其核心问题是贝叶斯网络结构学习.目前,基于依赖分析的贝叶斯网络结构学习方法主要存在三个问题:(1)需要进行大量的高维条件概率计算,(2)容易丢失弱联合依赖边,(3)对边的方向的确定具有局限性.针对这些问题,本文提出了首先进行递推条件独立性检验,然后进行因果语义定向,最后进行冗余边检验的贝叶斯网络结构学习方法.该方法能够有效地避免这些问题,更准确地建立马尔科夫毯预测. 相似文献
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基于贝叶斯网络的信用卡客户价值预测 总被引:1,自引:0,他引:1
在阐述贝叶斯网络的特点和学习算法的基础上,利用先验知识选取数据样本的属性变量,通过基于K2算法的贝叶斯网络结构学习和基于极大似然方法的参数学习,建立预测模型并进行银行信用卡客户价值预测。预测结果的正确率和覆盖率表明,贝叶斯网络是信用卡客户价值预测的有效工具。 相似文献
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贝叶斯网络是进行联合概率分解及研究证据传递的有效的图形模式.在贝叶斯网络中,研究变量的最优预测问题对揭示贝叶斯网络内部机制及分类器的属性选择等都具有重要意义.证明了在0-1损失下,对贝叶斯网络中任一特定变量进行预测时,联合预测是最优预测,贝叶斯网络和该变量的马尔科夫毯预测也是最优预测,同时给出了马尔科夫边界的信息结构,并使用模拟数据进行了定性与定量分析. 相似文献
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基于贝叶斯网络的电信客户流失预测分析 总被引:6,自引:0,他引:6
电信客户流失分析常用的数据挖掘方法有自动聚类、决策树和人工神经网络,它们是采用数据本身来训练模型的,没有利用先验知识。电信客户流失是由客户心理、服务质量和对手竞争等诸多复杂的因素造成的,利用这些已有的先验知识,可以提高预测的精度。该文根据先验知识选取分析变量,采集样本数据,通过贝叶斯网络的结构学习和参数学习,建立客户流失模型并进行客户流失趋势预测,取得了比标准数据集更准确的结果,该结果和决策树方法的预测结果相比还具有较大的优势,说明贝叶斯网络是分析客户流失等不确定性问题的有效工具。 相似文献
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以西安市城市居民出行方式为研究对象,收集西安市部分区域城市居民出行的调查数据。利用获得的调查数据,综合运用相关性分析方法和K2算法进行贝叶斯网络的结构学习;应用贝叶斯参数估计方法进行贝叶斯网络的参数学习,建立了应用于西安城市居民出行方式分析的贝叶斯网络。应用所建网络分析了是否有私家车、居民性别、居民年龄和出行目的对西安城市居民出行方式的影响。研究结果表明,基于贝叶斯网络建立的西安城市居民出行方式分析模型预测精度较高,具有较高的实用价值。 相似文献
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Forecasting the foreign exchange rate is an uphill task. Numerous methods have been used over the years to develop an efficient and reliable network for forecasting the foreign exchange rate. This study utilizes recurrent neural networks (RNNs) for forecasting the foreign currency exchange rates. Cartesian genetic programming (CGP) is used for evolving the artificial neural network (ANN) to produce the prediction model. RNNs that are evolved through CGP have shown great promise in time series forecasting. The proposed approach utilizes the trends present in the historical data for its training purpose. Thirteen different currencies along with the trade-weighted index (TWI) and special drawing rights (SDR) is used for the performance analysis of recurrent Cartesian genetic programming-based artificial neural networks (RCGPANN) in comparison with various other prediction models proposed to date. The experimental results show that RCGPANN is not only capable of obtaining an accurate but also a computationally efficient prediction model for the foreign currency exchange rates. The results demonstrated a prediction accuracy of 98.872 percent (using 6 neurons only) for a single-day prediction in advance and, on average, 92% for predicting a 1000 days’ exchange rate in advance based on ten days of data history. The results prove RCGPANN to be the ultimate choice for any time series data prediction, and its capabilities can be explored in a range of other fields. 相似文献
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汇率趋势预测数据挖掘的数据预处理方法 总被引:1,自引:1,他引:1
要用数据挖掘的方法得到有关汇率变动的趋势预测,其首要任务就是对这些汇市消息摘要进行一定的数据预处理,将其转换成具有一定结构的、有利于数据挖掘方法实现的目标语言。该文就是一个这样的对汇市消息摘要的预处理方法,它对有关汇市消息摘要的领域知识进行了详细的分析,得出了相应的领域规则知识,并对基于TRIE索引树的分析词典机制犤2犦加以改进,建立了一定的相关算法,从而实现了从汇市消息摘要到Bayes语言的数据挖掘的数据预处理。 相似文献
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We discuss multivariate time series signal processing that exploits a recently introduced approach to dynamic sparsity modelling based on latent thresholding. This methodology induces time-varying patterns of zeros in state parameters that define both directed and undirected associations between individual time series, so generating statistical representations of the dynamic network relationships among the series. Following an overview of model contexts and Bayesian analysis for dynamic latent thresholding, we exemplify the approach in two studies: one of foreign currency exchange rate (FX) signal processing, and one in evaluating dynamics in multiple electroencephalography (EEG) signals. These studies exemplify the utility of dynamic latent threshold modelling in revealing interpretable, data-driven dynamics in patterns of network relationships in multivariate time series. 相似文献
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In recent years forecasting of financial data such as interest rate, exchange rate, stock market and bankruptcy has been observed to be a potential field of research due to its importance in financial and managerial decision making. Survey of existing literature reveals that there is a need to develop efficient forecasting models involving less computational load and fast forecasting capability. The present paper aims to fulfill this objective by developing two novel ANN models involving nonlinear inputs and simple ANN structure with one or two neurons. These are: functional link artificial neural network (FLANN) and cascaded functional link artificial neural network (CFLANN). These have been employed to predict currency exchange rate between US$ to British Pound, Indian Rupees and Japanese Yen. The performance of the proposed models have been evaluated through simulation and have been compared with those obtained from standard LMS based forecasting model. It is observed that the CFLANN model performs the best followed by the FLANN and the LMS models. 相似文献
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We address the problem of generating normative forecasts efficiently from a Bayesian belief network. Forecasts are predictions of future values of domain variables conditioned on current and past values of domain variables. To address the forecasting problem, we have developed a probability forecasting methodology, Dynamic Network Models (DNMs), through a synthesis of belief network models and classical time-series models. The DNM methodology is based on the integration of fundamental methods of Bayesian time-series analysis, with recent additive generalizations of belief network representation and inference techniques.We apply DNMs to the problem of forecasting episodes of apnea, that is, regular intervals of breathing cessation in patients afflicted with sleep apnea. We compare the one-step-ahead forecasts of chest volume, an indicator of apnea, made by autoregressive models, belief networks, and DNMs. We also construct a DNM to analyse the multivariate time series of chest volume, heart rate and oxygen saturation data. 相似文献
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基于预测能力的连续贝叶斯网络结构学习 总被引:3,自引:0,他引:3
通过对连续随机变量之间预测能力及其计算方法的讨论,提出基于预测能力的连续贝叶斯网络结构学习方法。该方法包括两个步骤,每个步骤都伴随环路检验。首先建立初始贝叶斯网络结构,其次调整初始贝叶斯网络结构,包括增加丢失的弧、删除多余的弧及调整弧的方向,并使用模拟数据进行了对比实验,结果表明该方法非常有致。 相似文献
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This study provides an examination of the effect of public news on inter-day exchange-rate return volatility. Unlike previous studies, the impacts ofboth U.S. and foreign macroeconomic news announcements are examined in the currency futures market for the Japanese yen, British pound, and Deutsche mark. Diffusion and jump-diffusion process models are developed which contain parameters conditional on the release of news. These models are estimated using the method of maximum likelihood, and are tested versus unconditional diffusion and jump-diffusion models using likelihood ratio tests. The results reveal that conditional variance diffusion and jump-diffusion process models dominate the equivalent non-conditional models. Over the period studied (January 1988–December 1990) U.S. merchandise trade balance and industrial production announcements had a significantly greater impact on trading period volatility than money supply or inflation announcements did. Foreign news was also found to have a substantially lower effect on foreign trading-period variance than U.S. news had on U.S. trading period variance. In addition, the correlation between the yen, pound, and mark was highest on days of U.S. macroeconomic news. Thus, this study provides evidence that the currency return generating process is not characterized by a simple diffusion process over trading and non-trading periods. Further, the release of U.S. and foreign macroeconomic news has been shown to provide additional understanding of the currency return process over and above that of more complex models such as a jump-diffusion process. 相似文献
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针对在实际应用中,需要根据不同的对象建立不同的贝叶斯网络来解决预测问题,设计并开发了贝叶斯网络预测平台,介绍了平台的结构和功能,重点介绍了平台实现时网络的数字化和网络拓扑结构的问题.利用数字形式描述网络的全部信息,用关系矩阵直观的描述节点间的依赖关系,并据此确定网络的拓扑结构,利用基于随机数的仿真算法对网络进行推理.该平台简单易用,为贝叶斯网络的建立和推理提供了一个通用的运行环境. 相似文献