e-ISSN 2231-8542
ISSN 1511-3701

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Parallel Exponential Smoothing Using the Bootstrap Method in R for Forecasting Asteroid's Orbital Elements

Lala Septem Riza, Judhistira Aria Utama, Syandi Mufti Putra, Ferry Mukharradi Simatupang and Eddy Prasetyo Nugroho

Pertanika Journal of Tropical Agricultural Science, Volume 26, Issue 1, January 2018

Keywords: Exponential smoothing, orbital element, parallel computing, R programming language, time series analysis

Published on: 18 Jan 2018

Nowadays, large datasets become main intentions of researchers in many areas. However, a challenge that still remains mainly unresolved is the lack of strategies used for analysing large time-series datasets in parallel. Therefore, this research aims to design a model of exponential smoothing working on parallel computing by using the bootstrap method. Three parts will be considered in the model: data pre-processing using the bootstrap methods, parallel exponential smoothing, and aggregation of results to be the final predicted values. To implement the processes, some packages available in the R environment such as "foreach", "forecast" and "doParallel" are utilised. R environment provides many packages for scientific computing, data analysis, time-series analysis and high performance computing. For testing and validating the proposed model and implementation, a case study in astronomy, i.e. the prediction of asteroid's orbital elements, was done. Moreover, a comparison and analysis with the results produced by algorithm of Regularized Mix Variable Symplectic 4 Yarkovsky Effect (RMVS4-YE) is also presented in this paper to provide a high level of confidence on the proposed model.

ISSN 1511-3701

e-ISSN 2231-8542

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