Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

Performance Evaluation of AIC and BIC in Time Series Clustering with Piccolo Method

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  • @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
Triyani Hendrawati1,*, Aji Hamim Wigena2, I Made Sumertajaya2, Bagus Sartono2
  • 1: Department of Statistics Padjadjaran University, Bandung, Indonesia
  • 2: Department of Statistics IPB University (Bogor Agricultural University), Bogor, Indonesia
*Contact email: triyani.hendrawati@gmail.com

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).