IJAIN (International Journal of Advances in Intelligent Informatics) | |
Clustering stationary and non-stationary time series based on autocorrelation distance of hierarchical and k-means algorithms | |
Dian Sukma Pratiwi1  Kartika Fithriasari2  Aldho Riski Irawan2  Mohammad Alfan Alfian Riyadi2  | |
[1] Departement of Actuarial Science, Bandung;Departement of Statistics, Institut Teknologi Sepuluh Nopember; | |
关键词: Autocorrelation Distance; Hierarchical Algorithm; K-Means Algorithm; Non Stationary Time Series; Stationary Time Series; | |
DOI : 10.26555/ijain.v3i3.98 | |
来源: DOAJ |
【 摘 要 】
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.
【 授权许可】
Unknown