دانلود رایگان مقاله لاتین آنالیز بیزی سری زمانی از سایت الزویر
عنوان فارسی مقاله:
تجزیه و تحلیل بیزی سری زمانی با استفاده از روش محاسبات دانه
عنوان انگلیسی مقاله:
Bayesian analysis of time series using granular computing approach
سال انتشار : 2014
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مقدمه انگلیسی مقاله:
1. Introduction
The Bayesian methods for the time series analysis are proven successful in many practical applications, e.g., [1–3]. Nonetheless, the Bayesian time series analysis and probability theory are not meant to process directly the information described in natural language, and according to Zadeh [4], people granulate information and operate mainly on words and propositions easy to express in natural language. Hopefully, the soft computing methods, especially data mining, enable to retrieve and process the conceptually meaningful human-consistent information from large datasets. Such information entities have given rise to the general framework of Granular Computing [5,6]. Although, time series data mining with the use of the soft computing tools has gained a lot of attention in the literature, the interdisciplinary combination of soft computing and Bayesian approach does not seem to be extensively investigated. As presented in Table 1, according to the keyword and abstract search, there are over 2.7k articles on ‘Bayesian’ AND ‘time series’, and over 2.3k results about ‘granular computing’. However, only 1 result combing these keywords. Furthermore, the Scopus search engine returns only 6 positions for the keywords ‘soft computing’ AND ‘time series’ AND ‘Bayesian’.The objective of this paper is to review the related work on the application of the soft computing methods for the time series analysis, and to propose the conceptual Bayesian Granular Computing (B-GC) framework for Time Series Forecasting. The goal is to incorporate the human-consistent soft computing methods, especially data mining and classification techniques, in the construction of the prior model probability distributions. The proposed approach assumes employing techniques from the following research fields: the fuzzy sets theory, the knowledge discovery from sequential data, especially the time series abstraction and mining for linguistic summaries, Support Vector Machines for the classification problem and the Markov Chain Monte Carlo methods for the posterior simulation and the Bayesian inference. The approach is in line with the Generalized Theory of Uncertainty [7].
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کلمات کلیدی:
Katarzyna Kaczmarek - Google Scholar Citations scholar.google.com/citations?user=V8TO3tEAAAAJ&hl=en Bayesian analysis of time series using granular computing approach. O Hryniewicz, K ... Human input about linguistic summaries in time series forecasting. [PDF]Human Input about Linguistic Summaries in Time Series ... - ThinkMind https://www.thinkmind.org/download.php?articleid=achi_2015_1_20_20156 by K Kaczmarek - Cited by 3 - Related articles time series and sequence models; Bayesian methods; supervised learning. ... [7], the time series analysis starts with the identification of the probabilistic model ... Linguistic summaries are an example of information granules, and mining for ... Applied Bayesian Forecasting and Time Series Analysis https://books.google.com/books?isbn=0412044013 Andy Pole, Mike West, Jeff Harrison - 1994 - Mathematics ... is special about a time series compared with data that does not have a time component. We examine the nature of time series analysis and forecasting, discuss the importance of dynamic systems, and explore the nature of Bayesian modelling. ... by more than one unit on this granular scale, missing values are recorded. [PDF]Soft Computing Methods in the Bayesian Analysis of Time Series fcds.cs.put.poznan.pl/Seminaria/2016_SemPP_Kaczmarek.pdf Mar 8, 2016 - time series basing on the information retrieved with the soft computing ... especially the generation of meaningful information granules and more ... The Bayesian analysis is proven successful in many practical applications for. Forecasting of Short Time Series with Intelligent Computing - Springer link.springer.com/chapter/10.1007%2F978-3-319-30165-5_4 Mar 26, 2016 - The inclusion of prior knowledge may be easily formalized with the Bayesian approach. However, the proper formulation of prior probability ... CiteSeerX — Polish Academy citeseerx.ist.psu.edu/viewdoc/citations;jsessionid...?doi=10.1.1.673.7258 We propose to represent time series in a human-consistent way using linguistic ... 2, Bayesian analysis of time series using granular computing approach ...
