Rule generation for categorical time series with Markov assumptions

Christian H. Weiss

首页> 外文期刊>Statistics and computing >Rule generation for categorical time series with Markov assumptions

【24h】

Rule generation for categorical time series with Markov assumptions

机译：具有马尔可夫假设的分类时间序列的规则生成

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

Several procedures of sequential pattern analysis are designed to detect frequently occurring patterns in a single categorical time series (episode mining). Based on these frequent patterns, rules are generated and evaluated, for example, in terms of their confidence. The confidence value is commonly interpreted as an estimate of a conditional probability, so some kind of stochastic model has to be assumed. The model is identified as a variable length Markov model. With this assumption, the usual confidences are maximum likelihood estimates of the transition probabilities of the Markov model. We discuss possibilities of how to efficiently fit an appropriate model to the data. Based on this model, rules are formulated. It is demonstrated that this new approach generates noticeably less and more reliable rules.

机译：设计了几种顺序模式分析过程，以检测单个分类时间序列中的频繁发生模式（事件挖掘）。基于这些频繁的模式，可以根据规则的置信度来生成和评估规则。置信度值通常被解释为对条件概率的估计，因此必须采用某种随机模型。该模型被识别为可变长度马尔可夫模型。在此假设下，通常的置信度是马尔可夫模型的转移概率的最大似然估计。我们讨论了如何有效地将适当的模型拟合到数据的可能性。基于此模型，制定了规则。事实证明，这种新方法生成的规则明显更少，更可靠。

著录项

来源
《Statistics and computing》 |2011年第1期|p.1-16|共16页
作者
Christian H. Weiss;
展开▼
作者单位

Department of Statistics, Institute of Mathematics,University of Wuerzburg, Wuerzburg, Germany;

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类
关键词
variable length markov model; sequential pattern analysis; episode mining; categorical time series;

机译：变长马尔可夫模型;顺序模式分析;情节挖掘;分类时间序列;

相似文献

外文文献
中文文献
专利

1. Hidden Markov Models for Time Series : An Introduction Using R . Walter ? Zucchini , Iain L. ? McDonald , and Roland ? Langrock , Boca Raton , CRC Press Hidden Markov Models for Time Series Hidden Markov Models for Time Series : An Introduction Using R An Introduction Using R . Walter ? Zucchini Walter Walter ? Zucchini Zucchini , Iain L. ? McDonald Iain L. Iain L. ? McDonald McDonald , and Roland ? Langrock Roland Roland ? Langrock Langrock , Boca Raton Boca Raton , CRC Press CRC Press [J] . Patterson Toby Biometrics: Journal of the Biometric Society : An International Society Devoted to the Mathematical and Statistical Aspects of Biology . 2019,第2期

机译：隐藏的马尔可夫模型时间序列：使用r的简介。沃尔特？西葫芦，Iain L.？麦当劳和罗兰？ Langrock，Boca Raton，CRC压力隐马尔可夫模型用于时间序列隐藏式马尔可夫型号的时间序列：使用R引言使用R引言。沃尔特？西葫芦沃尔特沃尔特？西葫芦夏南瓜，Iain L.？麦当劳Iain L. Iain L.？麦当劳麦当劳，罗兰？ Langrock Roland Roland？ Langrock Langrock，Boca Raton Boca Raton，CRC按CRC压力机
2. Multiple hidden Markov models for categorical time series [J] . Colombia R., Giordano S. Journal of Multivariate Analysis: An International Journal . 2015,第Null期

机译：用于分类时间序列的多个隐藏马尔可夫模型
3. Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series [J] . Juliane Weber, Christopher Zachow, Dirk Witthaut Physical review, E . 2018,第3aPta1期

机译：使用添加剂二元马尔可夫链模拟长相关时间：风发时序列的应用
4. Factorizing Markov Models for Categorical Time Series Prediction [C] . Christoph Freudenthaler, Steffen Rendle, Lars Schmidt-Thieme International Conference of Numerical Analysis and Applied Mathematics . 2011

机译：分解马尔可夫模型进行分类时间序列预测
5. Time Series Models for Multivariate Binary and Categorical Data [D] . Reichmann, Lena Christine. 2021

机译：多变量二进制和分类数据的时间序列模型
6. Visualization of Categorical Longitudinal and Times Series Data [O] . Stephen J. Tueller, Richard A. Van Dorn, Georgiy V. Bobashev -1

机译：分类纵向和时间序列数据的可视化
7. Multiple Hidden Markov Models for Categorical Time Series [O] . Roberto Colombi, Sabrina Giordano 2014

机译：分类时间序列的多重隐马尔可夫模型
8. Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes [R] . Cohen, P. , Heeringa, B. , Adams, N. 2002

机译：用于将分类时间序列划分为剧集的无监督算法

Rule generation for categorical time series with Markov assumptions

摘要

著录项

相似文献

相关主题

期刊订阅