Investigating aerosol vertical distribution using CALIPSO time series over the Middle East and North Africa (MENA), Europe, and India: A BFAST-based gradual and abrupt change detection

2021 ◽  
Vol 264 ◽  
pp. 112619
Author(s):  
Foad Brakhasi ◽  
Mohammad Hajeb ◽  
Tero Mielonen ◽  
Aliakbar Matkan ◽  
Jan Verbesselt
Keyword(s):  
Time Series ◽  
Middle East ◽  
Vertical Distribution ◽  
Change Detection ◽  
North Africa ◽  
Abrupt Change ◽  
Abrupt Change Detection

scholarly journals A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Jin-Peng Qi ◽  
Jie Qi ◽  
Qing Zhang
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Large Scale ◽  
Abrupt Change ◽  
Binary Search ◽  
Binary Search Trees ◽  
Search Trees ◽  
Haar Wavelet Transform ◽  
Kolmogorov Statistic ◽  
Abrupt Change Detection

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS,t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.

Abrupt Change Detection for a Class of Known Systems

1989 ◽  
Vol 22 (6) ◽  
pp. 91-93
Author(s):  
K. Berbiche ◽  
J. Aguilar-Martin
Keyword(s):  
Change Detection ◽  
Abrupt Change ◽  
Abrupt Change Detection

scholarly journals Abrupt change detection with One-Class Time-Adaptive Support Vector Machines

2013 ◽  
Vol 40 (18) ◽  
pp. 7242-7249 ◽  
Author(s):  
Guillermo L. Grinblat ◽  
Lucas C. Uzal ◽  
Pablo M. Granitto
Keyword(s):  
Support Vector Machines ◽  
Change Detection ◽  
Abrupt Change ◽  
Support Vector ◽  
Class Time ◽  
Vector Machines ◽  
Adaptive Support ◽  
Abrupt Change Detection
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