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All Journal International Journal of Advances in Intelligent Informatics
Aldho Riski Irawan
Departement of Statistics, Institut Teknologi Sepuluh Nopember
Computer Science & IT

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Clustering stationary and non-stationary time series based on autocorrelation distance of hierarchical and k-means algorithms Mohammad Alfan Alfian Riyadi; Dian Sukma Pratiwi; Aldho Riski Irawan; Kartika Fithriasari
International Journal of Advances in Intelligent Informatics Vol 3, No 3 (2017): November 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i3.98

Abstract

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.
Co-Authors Dian Sukma Pratiwi Fithriasari, Kartika Mohammad Alfan Alfian Riyadi