{"title":"多分形的深度去趋势交叉相关分析算法","authors":"","doi":"10.1016/j.physa.2024.130105","DOIUrl":null,"url":null,"abstract":"<div><p>In the natural and social sciences, multifractal properties between two non-stationary time series are influenced not only by each other, but also by exogenous variables and historical data. However, traditional multifractal detrended cross-correlation analysis did not realize this problem, but directly explored the multifractal nature of time series. To eliminate the influence of exogenous variables and historical data as much as possible, the deep multifractal detrended cross-correlation analysis (DMF-DCCA) is developed to research the multifractal cross- correlation nature between two non-stationary time series. Furthermore, the effectiveness of DMF-DCCA has been validated using a simulated dataset and two real-world datasets.</p></div>","PeriodicalId":20152,"journal":{"name":"Physica A: Statistical Mechanics and its Applications","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep multifractal detrended cross-correlation analysis algorithm for multifractals\",\"authors\":\"\",\"doi\":\"10.1016/j.physa.2024.130105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In the natural and social sciences, multifractal properties between two non-stationary time series are influenced not only by each other, but also by exogenous variables and historical data. However, traditional multifractal detrended cross-correlation analysis did not realize this problem, but directly explored the multifractal nature of time series. To eliminate the influence of exogenous variables and historical data as much as possible, the deep multifractal detrended cross-correlation analysis (DMF-DCCA) is developed to research the multifractal cross- correlation nature between two non-stationary time series. Furthermore, the effectiveness of DMF-DCCA has been validated using a simulated dataset and two real-world datasets.</p></div>\",\"PeriodicalId\":20152,\"journal\":{\"name\":\"Physica A: Statistical Mechanics and its Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physica A: Statistical Mechanics and its Applications\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378437124006149\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica A: Statistical Mechanics and its Applications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378437124006149","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Deep multifractal detrended cross-correlation analysis algorithm for multifractals
In the natural and social sciences, multifractal properties between two non-stationary time series are influenced not only by each other, but also by exogenous variables and historical data. However, traditional multifractal detrended cross-correlation analysis did not realize this problem, but directly explored the multifractal nature of time series. To eliminate the influence of exogenous variables and historical data as much as possible, the deep multifractal detrended cross-correlation analysis (DMF-DCCA) is developed to research the multifractal cross- correlation nature between two non-stationary time series. Furthermore, the effectiveness of DMF-DCCA has been validated using a simulated dataset and two real-world datasets.
期刊介绍:
Physica A: Statistical Mechanics and its Applications
Recognized by the European Physical Society
Physica A publishes research in the field of statistical mechanics and its applications.
Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents.
Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.