Clustering stationary and non-stationary time series based on autocorrelation distance of hierarchical and k-means algorithms

IJAIN (International Journal of Advances in Intelligent Informatics)

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