Clustering Time Series Using Dynamic Time Warping Distance in Provinces in Indonesia Based on Rice Prices

Yeni Rahkmawati; Selvi  Annisa

doi:10.38043/tiers.v4i2.5081

Authors

Yeni Rahkmawati Universitas Lambung Mangkurat
Selvi Annisa Universitas Lambung Mangkurat

DOI:

https://doi.org/10.38043/tiers.v4i2.5081

Keywords:

Clustering time series, Dynamic time warping, Hierarchical clustering, Rice Prices, Indonesia

Abstract

Rice is a food commodity that is a basic need for Indonesian people. Since the end of 2022, average rice prices in Indonesia have been increasing, breaking the record for the highest price from August to October 2023. The price of rice in each province in Indonesia is different. This can happen because rice center provinces will distribute their rice production to other regions to meet rice needs. The grouping of provinces in Indonesia based on rice prices over time is an interesting thing to research. The analysis method used to group similar objects into groups for time series data is called clustering time series. The distance that can be used to measure the closeness of two-time series is the Dynamic Time Warping (DTW) distance. The clustering analysis used is the single, complete, average, Ward, and median linkage method. The results of the analysis show that time series clustering in provinces in Indonesia based on rice prices is best using median linkage hierarchical clustering. The median linkage method has a cophenetic correlation coefficient value of 0.899064, meaning that clustering using the DTW distance with the median difference is very good. The resulting clusters contained 3 clusters which had different characteristics between the clusters. There are 2 clusters that can be of concern to the government, because there are clusters that have rice prices that have always been high in the last period and there are provincial clusters that have rice prices that are very diverse or can be said to be unstable.

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