Treatment of Outlier Using Interpolation Method in Malaysia Tourist Arrivals

  • Authors

    • Maria Elena Nor
    • Norsoraya Azurin Wahir
    • G P. Khuneswari
    • Mohd Saifullah Rusiman
    2018-07-04
    https://doi.org/10.14419/ijet.v7i3.7.16207
  • Outliers, Box-Jenkins, Linear Interpolation, Cubic Spline Interpolation

  • Abstract

    The presence of outliers is an example of aberrant data that can have huge negative influence on statistical method under the assumption of normality and it affects the estimation. This paper introduces an alternative method as outlier treatment in time series which is interpolation. It compares two interpolation methods using performance indicator. Assuming outlier as a missing value in the data allows the application of the interpolation method to interpolate the missing value, thus comparing the result using the forecast accuracy. The monthly time series data from January 1998 until December 2015 of Malaysia Tourist Arrivals were used to deal with outliers. The results found that the cubic spline interpolation method gave the best result than the linear interpolation and the improved time series data indicated better performance in forecasting rather than the original time series data of Box-Jenkins model.

     

  • References

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  • How to Cite

    Elena Nor, M., Azurin Wahir, N., P. Khuneswari, G., & Saifullah Rusiman, M. (2018). Treatment of Outlier Using Interpolation Method in Malaysia Tourist Arrivals. International Journal of Engineering & Technology, 7(3.7), 51-53. https://doi.org/10.14419/ijet.v7i3.7.16207

    Received date: 2018-07-24

    Accepted date: 2018-07-24

    Published date: 2018-07-04