Forecasting of Air Passengers using Singular Spectrum Analysis

Volume 8, Issue 2, April 2023     |     PP. 51-61      |     PDF (1356 K)    |     Pub. Date: May 4, 2023
DOI: 10.54647/mathematics110393    90 Downloads     120789 Views  

Author(s)

Sisti Nadia Amalia, Department of Mathematics, State University of Medan, Indonesia
Zul Amry, Department of Mathematics, State University of Medan, Indonesia

Abstract
Air transportation is the most appropriate option for extremely vast distances, such as those between cities, provinces, and countries. While unpredictability, high volatility, and seasonality sometimes result in complex behavior in air passenger time series, this research applies the Singular Spectrum Analysis technique for air passengers data and uses the linear recurrent type for forecasting. Trends, seasonality, cyclists, and noise can all be found and extracted using Singular Spectrum Analysis. Singular Spectrum Analysis has the potential to be a highly effective forecasting method.

Keywords
Singular Spectrum Analysis, Linear Reccurent Forecasting, Air Passenger

Cite this paper
Sisti Nadia Amalia, Zul Amry, Forecasting of Air Passengers using Singular Spectrum Analysis , SCIREA Journal of Mathematics. Volume 8, Issue 2, April 2023 | PP. 51-61. 10.54647/mathematics110393

References

[ 1 ] A.Shlemov, N. Golyandina, D. Holloway and A.Spirov Shaped 3D Singular Spectrum Analysis for Quantifying Gene Expression, with Application to the Early Zebrafish Embryo,Biomed Research International,v.2015,p.1-18,(2015).
[ 2 ] Abraham B, Ledolter J, et al. Statistical Methods for Forecasting: John Willey and Sons, New York (1983).
[ 3 ] Bougas C. Forecasting air passenger traffic flows in Canada:An evaluation of time series models and combination methods. Quebec : Laval University, 2013.
[ 4 ] C. D. Lewis, Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Butterworth-Heinemann (1982).
[ 5 ] Golyandina N and A.Zhigljavsky, Singular Spectrum Analysis for Time Series. Springer,Heidelberg, (2013).
[ 6 ] Golyandina N, V.Nekritkuin and A.Zhigljavsky, Analysis of Time Series Structure,SSA and Related Techniques, Chapman & HALL /CRC (2001).
[ 7 ] Gumgum Darmawan, et al, Forecasting of Internet Usage by Singular Spectrum Analysis with Trend Extraction Method. Cite as: AIP Conference Proceedings 2192, 090002; https://doi.org/10.1063/1.5139172. Published Online: 19 December 2019
[ 8 ] Hassani H, Heravi S, Brown G, et al. Forecasting before, during, and after recession with singular spectrum analysis. Journal of Applied Statistics, 2013, 40(10):2290-2302
[ 9 ] Hassani H and Mahmoudvand R. Singular Spectrum Analysis Using R Macmillan Publishers Ltd. London (2018).
[ 10 ] Liang Xiaozhen, et al. An integrated forecasting model for air passenger traffic in China based on singular spectrum analysis. Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice, 2017, 37(6):1479-1488
[ 11 ] Sun, Y.; Zhang, G.; Yin, H. Passenger flow prediction of subway transfer stations based on nonparametric regression model. Discret. Dyn. Nat. Soc. 2014, 1–8.
[ 12 ] Tsui W, Balli H, Gilbey A, et al. Forecasting of Hong Kong airport's passenger throughput. Tourism Management, 2014, 42(6):62-76.
[ 13 ] Wei Zhou,et al, Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine. Sensors, 2020, 20 : 3555
[ 14 ] Xiao Y, Liu J J, Hu Y, et al. A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting[J]. Journal of Air Transport Management, 2014, 39(7):1-11.
[ 15 ] Badan Pusat Statistik, www.bps.go.id