Clustering the stationary and non stationary time series based on autocorrelation distance of hierarchical and K-Means algorithm

(1) * Mohammad Alfan Alfian Riyadi Mail (Departement of Statistics, Institut Teknologi Sepuluh Nopember, Indonesia)
(2) Dian Sukma Pratiwi Mail (Departement of Actuarial Science, Bandung, Indonesia)
(3) Aldho Riski Irawan Mail (Departement of Statistics, Institut Teknologi Sepuluh Nopember, Indonesia)
(4) Kartika Fithriasari Mail (Departement of Statistics, Institut Teknologi Sepuluh Nopember, Indonesia)
*corresponding author


Abstract


Observing large dimension time series could be time consuming. One identification and classification approach is a time series clustering. This research focuses on the clustering of stationary and non-stationary time series data. This study aimed to compare the accuracy of two algorithms: hierarchical cluster and K-Means cluster using ACF’s distance. 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 stationary and non-stationary data models. Thus, it can be concluded that the best algorithm for both stationary and non-stationary time series classification is K-Means algorithm.

Keywords


Autocorrelation Distance; Hierarchical Algorithm; K-Means Algorithm; Non Stationary Time Series; Stationary Time Series

   

DOI

https://doi.org/10.26555/ijain.v3i3.98
   

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International Journal of Advances in Intelligent Informatics
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