Alternative Method: Outlier Treatments with Box-Jenkins and Neural Network via Interpolation Method
Keywords:Outlier Treatment, Time Series, Forecasting, Linear Interpolation, Cubic Spline Interpolation
AbstractOutliers represent the points that greatly diverge and act differently from the rest of the points. These kinds of phenomenon usually happen in the data especially in time series data. The presence of this outlier gave bad effect in all statistical method including forecasting if there are no actions on it. Thus, this paper discusses alternative methods which are linear interpolation and cubic spline interpolation to the time series data as outlier treatment. Assuming outlier as missing value in the data, the outlier were detectedÂ and the results were compared using forecast accuracies by two popular forecasting model, Box-Jenkins and neural network. The monthly time series data of Malaysia tourist arrival were used in this paper from 1998 until 2015. The result indicates that the improved time series data using the linear interpolation and cubic spline interpolation showed great performance in forecasting than the original data series.
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How to Cite
Wahir, N. A., Nor, M. E., Rusiman, M. S., & Khuneswari, G. P. (2018). Alternative Method: Outlier Treatments with Box-Jenkins and Neural Network via Interpolation Method. Journal of Science and Technology, 10(2). Retrieved from https://publisher.uthm.edu.my/ojs/index.php/JST/article/view/3009
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