Finding Water Quality Trend Patterns Using Time Series Clustering: A Case Study

2019 ◽  
Author(s):  
Leijun Huang ◽  
Hailin Feng ◽  
Ying Le
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Time Series Clustering

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

Analysis of Surface Water Quality in Tehran

2002 ◽  
Vol 37 (2) ◽  
pp. 489-511 ◽  
Author(s):  
Golamreza Asadollah-Fardi
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Surface Water ◽  
Time Series Analysis ◽  
Surface Water Quality ◽  
Exponential Smoothing ◽  
Future Design ◽  
Series Analysis

Abstract Box-Jenkins and exponential smoothing time series analysis of the monthly water quality in surface water in Tehran was conducted as a case study. Various univariate models were developed for each determinand. Most of the models were seasonal, indicating that the water quality determinands vary throughout the year. The models follow the same pattern of variations present in the data. This study shows the credibility of the models, therefore the models may be used for future design purposes.

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.

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