Clustering stationary and non-stationary time series based on autocorrelation distance of hierarchical and k-means algorithms
2017 ◽
Vol 3
(3)
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pp. 154
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Keyword(s):
Observing large dimension time series could be time-consuming. One identification and classification approach is a time series clustering. This study aimed to compare the accuracy of two algorithms, hierarchical cluster and K-Means cluster, using ACF’s distance for clustering stationary and non-stationary time series data. This research uses both simulation and real datasets. The simulation generates 7 stationary data models and another 7 of non-stationary data models. On the other hands, the real dataset is the daily temperature data in 34 cities in Indonesia. As a result, K-Means algorithm has the highest accuracy for both data models.
Keyword(s):
2018 ◽
Vol 8
(4)
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pp. 2327
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Keyword(s):
1995 ◽
Vol 46
(12)
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pp. 1471-1480
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Keyword(s):
2014 ◽
Vol 2
(4(68))
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pp. 24
1978 ◽
Vol 5
(2)
◽
pp. 18-28
2016 ◽
Vol 15
(02)
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pp. 1650009
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Keyword(s):
Keyword(s):
2009 ◽
Vol 18
(3)
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pp. 675-693
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