Research Article
Performance Evaluation of AIC and BIC in Time Series Clustering with Piccolo Method
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290340, author={Triyani Hendrawati and Aji Hamim Wigena and I Made Sumertajaya and Bagus Sartono}, title={Performance Evaluation of AIC and BIC in Time Series Clustering with Piccolo Method}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={aic ar metric bic piccolo distance time series clustering}, doi={10.4108/eai.2-8-2019.2290340} }- Triyani Hendrawati
Aji Hamim Wigena
I Made Sumertajaya
Bagus Sartono
Year: 2020
Performance Evaluation of AIC and BIC in Time Series Clustering with Piccolo Method
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290340
Abstract
Piccolo method use parameters of Autoregressive model tocluster time series data. One set of time series data can produce several model, but only one model is used for clustering. Akaike’s Information Criterion (AIC) or Bayesian information Criterion (BIC) can be used to selection model. But if it is used different criterion to selection model, can be produced different model, so it can cause different cluster. The aim of this research is to evaluate performance of AIC and BIC in time series clustering with Piccolo method. The simulation comparing performance of AIC with BIC in time series clustering using the Piccolo method was carried out. Results shows that Bayesian information Criterion (BIC) is better than Akaike’s information Criterion (AIC).