Dynamic Time Warping Techniques for Time Series Clustering of Covid-19 Cases in DKI Jakarta


  • Meicheil Yohansa IPB University
  • Khairil Anwar Notodiputro IPB University
  • Erfiani IPB University




Dynamic Time Warping (DTW), time series, Covid-19


The number of positive cases of Covid-19 in DKI Jakarta has contributed to the national issues, reaching 25% of the total cases in Indonesia. The research examined and modeled the distribution pattern of Covid-19 positive cases in DKI Jakarta based on 44 districts spreading over six administrative areas. The data were regarding positive Covid-19 cases in DKI Jakarta for the past year, from April 2020 to April 2021. The research related to the pattern of positive Covid-19 distribution in 44 districts was carried out by time series clustering through Dynamic Time Warping (DTW) distances and agglomerative hierarchical methods. Then, the effectiveness of the clustering process is evaluated by comparing the predicted value of Covid-19 cases between clustering and non-clustering forecast results at the city level for the next 14 days through the Autoregressive Integrated Moving Average (ARIMA) model. The results group 44 districts into 6 optimal clusters based on the pattern of positive cases of Covid-19 in each district. The highest distribution rate is in cluster A, and the lowest is in cluster F. Geographical characteristics are also indicated by clusters A, B, E, and F. Then, the results show that the Mean Average Percentage Error (MAPE) value of the clustering model ranges from 16% to 20%. The difference between MAPE values to the non-clustering model implies that the forecasting accuracy is not far apart, which is in the round of 5%−6%.


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Author Biographies

Meicheil Yohansa, IPB University

Department of Statistics, Faculty of Mathematics and Natural Sciences

Khairil Anwar Notodiputro, IPB University

Department of Statistics, Faculty of Mathematics and Natural Sciences

Erfiani, IPB University

Department of Statistics, Faculty of Mathematics and Natural Sciences


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