Forecasting Modelling of Cockles in Malaysia by Using Time Series Analysis

  • Authors

    • Hani Nabihah Aziz
    • Mohd Saifullah Rusiman
    • Siti Noor Asyikin Mohd Razali
    • Abdul Wahab Abdulla
    • Nur Amira Azmi
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.30.22376
  • Auto regressive moving average (ARMA), Holt-Linear, Mean Square Error (MSE

  • Abstract

    Cockle farmed in Malaysia are from Anadara genes and Arcidae family which known as blood cockle. Normally, it was found in the farmed around mangrove estuary areas in the muddy and sandy shores. This study aims to predict the production of cockle to ensure sure the cockle supplies are synchronised with the demand. Then, based on the demand, the prediction result could be used to make decision either to import or export the cockle. The data were taken from the Department of Fisheries Malaysia (DFM) and it has cyclic pattern data. There are two methods used in this study which are Holt-Linear method and Auto regressive moving average (ARMA). In determining the best fitted model between the two methods, the mean square error (MSE) values will be compared and the lowest value of MSE will assign as the best model. Result shows that ARMA(1,1) is the best model compared to Holt-Linear. Therefore, ARMA(1,1) model will be used to forecast the production of cockle in Malaysia.

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  • How to Cite

    Aziz, H. N., Rusiman, M. S., Razali, S. N. A. M., Abdulla, A. W., & Azmi, N. A. (2018). Forecasting Modelling of Cockles in Malaysia by Using Time Series Analysis. International Journal of Engineering & Technology, 7(4.30), 488-491. https://doi.org/10.14419/ijet.v7i4.30.22376

    Received date: 2018-11-29

    Accepted date: 2018-11-29

    Published date: 2018-11-30