Optimize TOD Time-Division with Dynamic Time Warping Distance-based Non-Hierarchical Cluster Analysis

2021 ◽  
Vol 20 (5) ◽  
pp. 113-129
Author(s):  
Jae-Yeon Hwang ◽  
◽  
Minju Park ◽  
Yongho Kim ◽  
Woojin Kang
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Dynamic Time Warping ◽  
Hierarchical Cluster ◽  
Time Warping ◽  
Dynamic Time ◽  
Time Division

scholarly journals Estimation of COVID-19 Dynamics in the Different States of the United States during the First Months of the Pandemic

2021 ◽  
Vol 5 (1) ◽  
pp. 53
Author(s):  
Ignacio Rojas-Valenzuela ◽  
Olga Valenzuela ◽  
Elvira Delgado-Marquez ◽  
Fernando Rojas
Keyword(s):  
United States ◽  
Time Series ◽  
Dynamic Time Warping ◽  
Hierarchical Cluster ◽  
The United States ◽  
Time Warping ◽  
Precise Knowledge ◽  
Eastern Zone ◽  
Western Zone ◽  
Dynamic Time

Estimation of COVID-19 dynamics and its evolution is a multidisciplinary effort, which requires the unification of heterogeneous disciplines (scientific, mathematics, epidemiological, biological/bio-chemical, virologists and health disciplines to mention the most relevant) to work together towards a better understanding of this pandemic. Time series analysis is of great importance to determine both the similarity in the behavior of COVID-19 in certain countries/states and the establishment of models that can analyze and predict the transmission process of this infectious disease. In this contribution, an analysis of the different states of the United States will be carried out to measure the similarity of COVID-19 time series, using dynamic time warping distance (DTW) as a distance metric. A parametric methodology is proposed to jointly analyze infected and deceased persons. This metric allows comparison of time series that have a different time length, making it very appropriate for studying the United States, since the virus did not spread simultaneously in all the states/provinces. After a measure of the similarity between the time series of the states of United States was determined, a hierarchical cluster was created, which makes it possible to analyze the behavioral relationships of the pandemic between different states and to discover interesting patterns and correlations in the underlying data of COVID-19 in the United States. With the proposed methodology, nine different clusters were obtained, showing a different behavior in the eastern zone and western zone of the United States. Finally, to make a prediction of the evolution of COVID-19 in the states, Logistic, Gompertz and SIR models were computed. With these mathematical models, it is possible to have a more precise knowledge of the evolution and forecast of the pandemic.

scholarly journals Estimation of COVID-19 dynamics in the different states of the United States using Time-Series Clustering

2020 ◽  
Author(s):  
Fernando Rojas ◽  
Olga Valenzuela ◽  
Ignacio Rojas
Keyword(s):  
United States ◽  
Time Series ◽  
Dynamic Time Warping ◽  
Hierarchical Cluster ◽  
The United States ◽  
Time Warping ◽  
Precise Knowledge ◽  
Eastern Zone ◽  
Western Zone ◽  
Dynamic Time

AbstractEstimation of COVID-19 dynamics and its evolution is a multidisciplinary effort, which requires the unification of heterogeneous disciplines (scientific, mathematics, epidemiological, biological/bio-chemical, virologists and health disciplines to mention the most relevant) to work together in a better understanding of this pandemic. Time series analysis is of great importance to determine both the similarity in the behavior of COVID-19 in certain countries/states and the establishment of models that can analyze and predict the transmission process of this infectious disease. In this contribution, an analysis of the different states of the United States will be carried out to measure the similarity of COVID-19 time series, using dynamic time warping distance (DTW) as a distance metric. A parametric methodology is proposed to jointly analyze infected and deceased persons. This metric allows to compare time series that have a different time length, making it very appropriate for studying the United States, since the virus did not spread simultaneously in all the states/provinces. After a measure of the similarity between the time series of the states of United States was determined, a hierarchical cluster was created, which makes it possible to analyze the behavioral relationships of the pandemic between different states and to discover interesting patterns and correlations in the underlying data of COVID-19 in the United States. With the proposed methodology, nine different clusters were obtained, showing a different behavior in the eastern zone and western zone of the United States. Finally, to make a prediction of the evolution of COVID-19 in the states, Logistic, Gompertz and SIR model was computed. With these mathematical model it is possible to have a more precise knowledge of the evolution and forecast of the pandemic.

Flash evaporation-gas chromatographic fingerprints with hierarchical cluster analysis for the determination of cigarette flavors

2011 ◽  
Vol 29 (6) ◽  
pp. 549-553
Author(s):  
Jian JIANG ◽  
Jun YANG ◽  
Fangfang HUANG ◽  
Shiqiang XU ◽  
Xiaoqing WANG ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Flash Evaporation ◽  
Chromatographic Fingerprints
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