动态系统线性模型和贝叶斯分类器在促进可持续发展中的时间序列分类

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
A. A. Hamad, Faris Maher Ahmed, Mamoon Fattah Khalf, M. Thivagar
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引用次数: 0

摘要

研究目的:介绍了一种新的时间序列判别分析(DA)方法。本文探讨了动态线性模型和贝叶斯分类器在时间序列分类中的应用,以促进不同部门在可持续性方面的应用。方法:本文提出了一些计算机模拟研究,在这些研究中,我们生成了四种不同的情景,对应于各种动态线性模型(DLMs)的时间序列观测。在判别分析中,我们研究了估计模型方差的策略,并将BCDLM与其他常见分类器的性能进行了比较。这些数据集由实时序列(来自索尼AIBO机器人的数据和咖啡类型的光谱分析)和伪时间序列(来自瑞典叶子的数据适应时间序列)组成。我们还指出,该算法用于确定实际应用中的训练集和测试集。结果:考虑到本文所研究的实时序列,所获得的结果表明,所开发的参数化方法代表了这类数据分析问题的一个有希望的替代方案,当我们有相对于类中观测数量的大序列时,时间序列的观测在实践中是相当困难的,即使需要更深入的研究。结论:BCDLM与分类器1NN、RDA、NBND和NBK的结果相当,优于LDA和QDA方法。这为时间序列分类提供了强大的组合,可以在能源消耗、废物管理和资源分配等领域实现准确的预测和明智的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic system linear models and Bayes classifier for time series classification in promoting sustainabilitys
Research purpose: The current work introduces a novel method for time series discriminant analysis (DA). Proposing a version for the Bayes classifier employing Dynamic Linear Models, which we denote by BCDLM This article explores the application of DLMs and the Bayes Classifier in time series classification to promote application in sustainability across diverse sectors. Method: This paper presents some computer simulation studies in which we generate four different scenarios corresponding to time series observations from various Dynamic Linear Models (DLMs). In Discriminant Analysis, we investigated strategies for estimating variance in models and compared the performance of the BCDLM with other common classifiers. Such datasets are composed of real-time series (data from SONY AIBO Robot and spectrometry of coffee types) and pseudo-time series (data from Swedish leaves adapted for time series). We also point out that algorithm was used to determine training and test sets in real-world applications. Results: Considering the real-time series examined in this paper, The results obtained indicate that the parametric approach developed represents a promising alternative for this class of DA problems, with observations of time series in a situation that is quite difficult in practice when we have series with large sizes with respect to the number of observations in the classes, even though more thorough studies are required. Conclusions: It concludes that the BCDLM performed comparably to the results of the classifiers 1NN, RDA, NBND and NBK and superior to the methods LDA and QDA. This offers a powerful combination for time series classification, enabling accurate predictions and informed decision-making in areas such as energy consumption, waste management, and resource allocation.
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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