5 papers
A Correction for the Paper "Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis"
Hong-Yan Zhang, Haoting Liu, Rui-Jia Lin +1
The symplectic geometry mode decomposition (SGMD) is a powerful method for decomposing time series, which is based on the diagonal averaging principle (DAP) inherited from the sing…
Pulling Back Theorem for Generalizing the Diagonal Averaging Principle in Symplectic Geometry Mode Decomposition and Singular Spectrum Analysis
Hong-Yan Zhang, Haoting Liu, Zhi-Qiang Feng +4
The symplectic geometry mode decomposition (SGMD) is a powerful method for analyzing time sequences. The SGMD is based on the upper conversion via embedding and down conversion via…
High Order Expansion Method for Kuiper's Statistic in Goodness-of-fit Test
Hong-Yan Zhang, Zhi-Qiang Feng, Haoting Liu +2
Kuiper's statistic, a measure for comparing the difference of ideal distribution and empirical distribution, is of great significance in the goodness-of-fit test. However, Ku…
Typical Algorithms for Estimating Hurst Exponent of Time Sequence: A Data Analyst's Perspective
Hong-Yan Zhang, Zhi-Qiang Feng, Si-Yu Feng +1
The Hurst exponent is a significant metric for characterizing time sequences with long-term memory property and it arises in many fields. The available methods for estimating the H…
Fixed-Point Algorithms for Solving the Critical Value and Upper Tail Quantile of Kuiper's Statistics
Hong-Yan Zhang, Wei Sun, Xiao Chen +2
Kuiper's statistic is a good measure for the difference of ideal distribution and empirical distribution in the goodness-of-fit test. However, it is a challenging problem to solve…