scholarly journals Development of a Realistic Driving Cycle Using Time Series Clustering Technique for Buses: Thailand Case Study

2019 ◽  
Vol 23 (4) ◽  
pp. 49-65 ◽  
Author(s):  
Songwut Mongkonlerdmanee ◽  
Saiprasit Koetniyom
Keyword(s):  
Time Series ◽  
Driving Cycle ◽  
Time Series Clustering ◽  
Clustering Technique

scholarly journals Space–time series clustering: Algorithms, taxonomy, and case study on urban smart cities

2020 ◽  
Vol 95 ◽  
pp. 103857
Author(s):  
Asma Belhadi ◽  
Youcef Djenouri ◽  
Kjetil Nørvåg ◽  
Heri Ramampiaro ◽  
Florent Masseglia ◽  
...  
Keyword(s):  
Time Series ◽  
Smart Cities ◽  
Clustering Algorithms ◽  
Space Time ◽  
Time Series Clustering

scholarly journals Deep Time-Series Clustering: A Review

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3001
Author(s):  
Ali Alqahtani ◽  
Mohammed Ali ◽  
Xianghua Xie ◽  
Mark W. Jones
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
State Of The Art ◽  
Movement Behavior ◽  
Series Data ◽  
Time Series Clustering ◽  
Deep Time ◽  
Important Field ◽  
Time Series Data Analysis

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.

Time Series Clustering and Classification for Uncertainty Analysis by MAAP5 Code

2020 ◽  
Author(s):  
Ikuo Kinoshita
Keyword(s):  
Time Series ◽  
Uncertainty Analysis ◽  
Regression Models ◽  
Hydrogen Generation ◽  
Uncertainty Propagation ◽  
Time Series Clustering ◽  
Clustering Technique ◽  
Uncertainty Propagation Analysis ◽  
Propagation Analysis ◽  
Uncertainty Parameters

Abstract The MAAP5.04 code uncertainty analysis was carried out for the Power Burst Facility Severe Fuel Damage Test 1-4. Comparisons between experimental data and analysis results were focused on hydrogen generation. The uncertainty propagation analysis was conducted through random variations of input uncertainty parameters of phenomenological models whose ranges were determined by the MAAP5 Zion parameter file. The time series clustering technique using the mean-shift algorithm was applied to the data set generated by the uncertainty propagation analysis. It was confirmed that the code predicted well the hydrogen mass generated and the uncertainty bounds of the analysis included the measured hydrogen generation history. The time series clustering technique demonstrated that the key model parameters could be identified for classifying the uncertainty analysis results using a decision tree classifier. Furthermore, the regression models were constructed which predict the uncertainty of the hydrogen generation from the model uncertainty parameters by using the support vector regressions. The hold-out method of cross validation was applied to the regression models of the hydrogen generation to investigate the training error and the test error for the uncertainty prediction of the hydrogen generation.

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