Open AccessClustering of Commodity Inflation Pattern based on Estimated ARIMA Model
Author(s) -
Triyani Hendrawati,
Aji Hamim Wigena,
I Made Sumertajaya,
Bagus Sartono
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1863/1/012058
Subject(s) - cluster analysis , autoregressive integrated moving average , silhouette , computer science , data mining , series (stratigraphy) , hierarchical clustering , cluster (spacecraft) , single linkage clustering , similarity (geometry) , k medians clustering , inflation (cosmology) , fuzzy clustering , cure data clustering algorithm , time series , pattern recognition (psychology) , artificial intelligence , machine learning , physics , theoretical physics , paleontology , image (mathematics) , biology , programming language
Clustering is the stage that is carried out before further data analysis. There are many approach method that can be used for clustering time series data, one of which is the model-based approach. In this study, we clustering inflation data used the ARIMA model. The cluster model is carried out after obtaining the clusters. The similarity between time series is measured using the development of Piccolo distance. Furthermore, the Ward hierarchical method is used for clustering. The Silhouette averaging method is used to determine the optimal number of clusters. The cluster model can be used to represent individual models. The cluster model is more effective than creating all individual models.