This study aims to forecast air passenger and cargo demand of the Indian aviation industry using the autoregressive integrated moving average (ARIMA) and Bayesian structural time series (BSTS) models. We utilized 10 years’ (2009–2018) air passenger and cargo data obtained from the Directorate General of Civil Aviation (DGCA-India) website. The study assessed both ARIMA and BSTS models’ ability to incorporate uncertainty under dynamic settings. Findings inferred that, along with ARIMA, BSTS is also suitable for short-term forecasting of all four (international passenger, domestic passenger, international air cargo, and domestic air cargo) commercial aviation sectors. Recommendations and directions for further research in medium-term and long-term forecasting of the Indian airline industry were also summarized.
AbugaberD. (n.d.). Chapter 23: Using ARIMA for time series analysis. https://ademos.people.uic.edu/Chapter23.html
2.
AhmadzadeF. (2010). Model for forecasting passenger of airport. In Proceedings of the 2010 International Conference on Industrial Engineering and Operations Management Dhaka, Bangladesh. http://iieom.org/paper/Final%20Paper%20for%20PDF/235%20Farzane%20Ahmadzade.pdf
3.
AlekseevK. P. G., & SeixasJ. M. (2002). Forecasting the air transport demand for passengers with neural modelling. In Proceedings of VII Brazilian Symposium on Neural Networks. pp. 86–91. https://ieeexplore.ieee.org/xpl/conhome/8414/proceeding
4.
AndersonD. R., SweeneyD. J., WilliamsT. A., CammJ. D., CochranJ. J., FryM. J., & OhlmannJ. W. (2019). An introduction to management science: Quantitative approaches to decision making (15th ed.). Cengage Learning, Inc.
5.
BaFailA. O. (2004). Applying data mining techniques to forecast number of airline passengers in Saudi Arabia (Domestic and International Travels). Journal of Air Transportation, 9(1), 100–115.
6.
BaoY., XiongT., & HuZ. (2012). Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method. Discrete Dynamics in Nature and Society, 2012, 1–12. http://dx.doi.org/10.1155/2012/431512
7.
BasakM., WestM., & NarangS. P. S. (2013). Forecasting air cargo demand in India. International Journal of Engineering Science and Innovative Technology, 2(6), 391–401.
8.
BougasC. (2013). Forecasting air passenger traffic flows in Canada: An evaluation of time series models and combination methods (Master’s thesis, Université Laval). https://corpus.ulaval.ca/jspui/handle/20.500.11794/24286
9.
BoxG. E. P., & JenkinsG. M. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
10.
BoxG. E. P., & JenkinsG. M. (1976). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.
11.
BrodersenK. H., GallusserF., KoehlerJ., RemyN., & ScottS. L. (2015). Inferring causal impact using bayesian structural time-series models. The Annals of Applied Statistics, 9(1), 247–274. https://doi.org/10.1214/14-AOAS788
12.
ChenS.C., KuoS. Y., ChangK. W., & WangY. T. (2012). Improving the forecasting accuracy of air passenger and air cargo demand: The application of back-propagation neural networks. Transportation Planning Technology, 35(3), 373–392. https://doi.org/10.1080/03081060.2012.673272.
13.
DeWittM. (2018). Bayesian time series analysis with bsts. https://michaeldewittjr.com/dewitt_blog/posts/2018-07-05-bayesian-time-series-analysis-with-bsts_files/
14.
DickeyD. A., & FullerW. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 49(4), 1057–1072. https://doi.org/10.2307/1912517
15.
Directorate General of Civil Aviation. (n.d.). Yearly statistics. {dataset} http://www.dgca.nic.in/reports/rep-ind.htm
16.
Directorate General of Civil Aviation. (2011). Invitation for expression of interest (EOI) for shortlisting of agency for developing a model to forecast air traffic. http://www.dgca.nic.in/tenders/ten-ind.htm
17.
ElprinceN. (2014). Air passengers forecasthttps://rpubs.com/nohaelprince/47545
18.
HorikoshiM., & TangY. (2016). ggfortify: Data visualization tools for statistical analysis results. https://CRAN.R-project.org/package=ggfortify
19.
HyndmanR. J., & KhandakarY. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3), 1–22. https://doi.org/10.18637/jss.v027.i03
20.
HyndmanR., AthanasopoulosG., BergmeirC., CaceresG., ChhayL., O’Hara-WildM., PetropoulosF., RazbashS., WangE., & YasmeenF. (2019). Forecast: Forecasting functions for time series and linear models. R package version 8.8. http://pkg.robjhyndman.com/forecast.
21.
International Air Transport Association. (2018). India’s air transport sector. https://www.iata.org/publications/economics/Reports/India-aviation-summit-Aug18.pdf
22.
International Civil Aviation Organization. (2006). Manual on air traffic forecasting. Doc 8991 AT/722/3. https://www.icao.int/MID/Documents/2014/Aviation%20Data%20Analyses%20Seminar/8991_Forecasting_en.pdf
23.
KimS., & ShinD. H. (2016). Forecasting short-term air passenger demand using big data from search engine queries. Automation in Construction, 70, 98–108. https://doi.org/10.1016/j.autcon.2016.06.009
24.
Kimnewzealand. (2017). Time series analysis and modeling with the air passengers dataset. http://rstudio-pubs-static.s3.amazonaws.com/311446_08b00d63cc794e158b1f4763eb70d43a.html
25.
KitamuraA. (2018). Forecasting Japan’s spot LNG prices using Bayesian structural time series (Masters thesis, Erasmus University Rotterdam)https://thesis.eur.nl/pub/43636/Kitamura-Ai.pdf
26.
LarsenK. (2016). Sorry ARIMA, but I’m going Bayesian’. https://multithreaded.stitchfix.com/blog/2016/04/21/forget-arima/
27.
LiuJ., NordmanD. J., & MeekerW. Q. (2016). The number of MCMC draws needed to compute Bayesian credible bounds. The American Statistician, 70(3), 275–284. https://doi.org/10.1080/00031305.2016.1158738.
28.
MajraH., SaxenaR., JhaS., & JagannathanS. (2016). Structuring technology applications for enhanced customer experience: Evidence from Indian air travellers. Global Business Review, 17(2), 351–374.
29.
MarazzoM., ScherreR., & FernandesE. (2010). Air transport demand and economic growth in Brazil: A time series analysis. Transportation Research Part E: Logistics and Transportation Review, 46(2), 261–269. https://doi.org/10.1016/j.tre.2009.08.008
30.
MingW., BaoY., HuZ., & XiongT. (2014). Multistep-ahead air passengers traffic prediction with hybrid ARIMA-SVMs models. The Scientific World Journal, 2014, 1–14. https://doi.org/10.1155/2014/567246.
31.
Minitab Express. (n.d.). Interpret all statistics and graphs for trend analysis. https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/time-series/how-to/trend-analysis/interpret-the-results/all-statistics-and-graphs/
32.
Mohie El-DinM. M., FaragM. S., & AbouzeidA. A. (2017). Airline passenger forecasting in EGYPT (Domestic and international). International Journal of Computer Applications, 165(6), 1–5. https://www.ijcaonline.org/archives/volume165/number6/eldin-2017-ijca-913113.pdf
33.
NauR. F. (2005). Introduction to ARIMA: Nonseasonal models. https://people.duke.edu/∼rnau/411arim.html
34.
NietoM. R., & Carmona BenitezR. (2015). An ARIMA-GARCH-bootstrap based method applied for forecasting the air passenger demand. In: 35th International Symposium on forecasting. https://forecasters.org/wp-content/uploads/gravity_forms/7-621289a708af3e7af65a7cd487aee6eb/2015/07/NietoDelfin_MariaRosa_ISF20151.pdf
35.
OlaniyiA. A., AdedotunK. O., & SamuelO. A. (2018). Forecasting methods for domestic air passenger demand in Nigeria. Journal Applied Research in Industrial Engineering, 5(2), 146–155. https://doi.org/10.22105/JARIE.2018.133561.1038
36.
OrdK., & LoweS. (1996). Automatic forecasting. The American Statistician, 50(1), 88–94. https://doi.org/10.1080/00031305.1996.10473549
37.
PalachyS. (2019). Stationarity in time series analysis. https://towardsdatascience.com/stationarity-in-time-series-analysis-90c94f27322
38.
QiuJ., Rao JammalamadakaS., & NingN. (2018). Multivariate Bayesian structural time series model. Journal of Machine Learning Research, 19(68), 1–33. http://www.jmlr.org/papers/volume19/18-009/18-009.pdf
39.
RengarajuV. R., & Thamizh ArasanV. (1992). Modeling for air travel demand. Journal of Transportation Engineering, 118(3), 371–380.
40.
Ruiz-AguilarJ. J., TuriasI. J., & Jiménez-ComeM. (2014). Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting. Transportation Research Part E: Logistics and Transportation Review, 67, 1–13. https://doi.org/10.1016/j.tre.2014.03.009
41.
R Core Team. (2019). R: A language and environment for statistical computing, R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/
42.
RStudio Team. (2018). RStudio: Integrated development for R. RStudio, Inc. http://www.rstudio.com/
43.
RobertsonM., & WallinM. (2014). Forecasting monthly air passenger flows from Sweden (Evaluating forecast performance using the airline model as benchmark) (Bachelors thesis, Uppsala University). https://www.diva-portal.org/smash/get/diva2:784998/FULLTEXT01.pdf
44.
SarkarR., & BaisyaR. K. (2005). Some aspects of market dynamics and customer satisfaction in the Indian domestic airlines sector. Global Business Review, 6(1), 113–124.
45.
ScottS. L. (2017). Fitting Bayesian structural time series with the bsts R package’. http://www.unofficialgoogledatascience.com/2017/07/fitting-bayesian-structural-time-series.html
46.
ScottS. L. (2019). bsts: Bayesian structural time series. R package version 0.9.1. https://CRAN.R-project.org/package=bsts
47.
ScottS. L., & VarianH. R. (2012). Bayesian variable selection for nowcasting economic time series. http://people.ischool.berkeley.edu/∼hal/Papers/2012/fat.pdf
48.
ScottS. L., & VarianH. R. (2013). Predicting the present with Bayesian structural time series. http://people.ischool.berkeley.edu/∼hal/Papers/2013/pred-present-with-bsts.pdf
49.
SharmaH. K., KumariK., & KarS. (2018). Air passengers forecasting for Australian airline based on hybrid rough set approach. Journal of Applied Mathematics, Statistics and Informatics, 14(1), 5–18. https://doi.org/10.2478/jamsi-2018-0001
50.
SpeddingT. A., & ChanK. K. (2000). Forecasting demand and inventory management using Bayesian time series. Integrated Manufacturing Systems, 11(5), 331–339. https://doi.org/10.1108/09576060010335609
51.
SrinidhiS., & ManraiA. K. (2014). International air transport demand: Drivers and forecasts in the Indian context. Journal of Modelling in Management, 9(3), 245–260. https://doi.org/10.1108/JM2-08-2013-0036
52.
SrisaengP., BaxterG., RichardsonS., & WildG. (2015). A forecasting tool for predicting Australia’s domestic airline passenger demand using a genetic algorithm. Journal of Aerospace Technology and Management, 7(4), 476–489. https://doi.org/10.5028/jatm.v7i4/475
53.
SteelM. F. J. (2010). Bayesian time series analysis. In Macroeconometrics and time series analysis (pp. 35–45). https://doi.org/10.1057/9780230280830_4
54.
StellwagenE., & TashmanL. (2013). ARIMA: The models of Box and Jenkins. Foresight: The International Journal of Applied Forecasting, (30), 28–33.
55.
TangY., HorikoshiM., & LiW. (2016). Ggfortify: Unified interface to visualize statistical results of popular r packages. The R Journal, 8.2, 478–489.
56.
TraplettiA., & HornikK. (2019). tseries: Time series analysis and computational finance. R package version 0.10–47. https://CRAN.R-project.org/package=tseries
57.
VanderkamD., AllaireJ. J., OwenJ., GromerD., & ThieurmelB. (2018). dygraphs: Interface to ‘dygraphs’ Interactive time series charting library. R package version 1.1.1.6. https://CRAN.R-project.org/package=dygraphs
58.
VarianH. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28. https://doi.org/10.1257/jep.28.2.3
59.
WickhamH. (2016). Package ‘ggplot2’: Elegant graphics for data analysis. Springer-Verlag. https://doi.org/10.1093/bioinformatics/btr406
60.
WickhamH., FrançoisR., HenryL., & MüllerK. (2019). dplyr: A Grammar of data manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr
61.
World Bank. (n.d.). Air transport, passengers carried. https://data.worldbank.org/indicator/IS.AIR.PSGR?end=2018&most_recent_value_desc=true&start=1970&view=chart
62.
XuS., ChanH. K., & ZhangT. (2019). Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach. Transportation Research Part E: Logistics and Transportation Review, 122, 169–180. https://doi.org/10.1016/j.tre.2018.12.005
