Autosoft Journal

Online Manuscript Access


Short-term Forecasting of Air Passengers based on Hybrid Rough Set and Double Exponential Smoothing Models



Abstract

This article focuses on the use of rough set theory in the modeling of time series forecasting. In this paper, we have used double exponential smoothing (DES) model for forecasting. The classical DES model has been improved by using the rough set technique. The improved double exponential smoothing (IDES) method can be used for the time series data without any statistical assumptions. The proposed method is applied on tourism demand of air transportation passenger data set in Australia and the results are compared with classical DES model. It has been observed that he forecasting accuracy of the proposed model is better than that of the classical model.


Keywords


Pages

Total Pages: 13

DOI
10.31209/2018.100000036


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways


Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Published

Online Article

Cite this document


References

J. C. R. Alcantud. (2016). Some formal relationships among soft sets, fuzzy sets, and their extensions. International Journal of Approximate Reasoning, 68, 45-53. https://doi.org/10.1016/j.ijar.2015.10.004

J. C. R. Alcantud, R. D. A. Calle, & M. J. M. Torrecillas. (2016). Hesitant Fuzzy Worth: An innovative ranking methodology for hesitant fuzzy subsets. Applied Soft Computing, 38, (2016) 232-243. https://doi.org/10.1016/j.asoc.2015.09.035

K. P. G. Alekseev & J. M. Seixas. (2009). A multivariate neural forecasting modeling for air transport - Pre-processed by decomposition: A Brazilian application. Journal of Air Transport Management, 15, 212-216. https://doi.org/10.1016/j.jairtraman.2008.08.008

A. An, Y. Huang, X. Huang, & N. Cercone. (2004). Feature Selection with Rough Sets for Web Page Classification. James F. Peters, A. Skowron. Transactions on Rough Sets II (Rough Sets and Fuzzy Sets). Springer, 1-13. https://doi.org/10.1007/978-3-540-27778-1_1

Changqing, Zeng, and Long Chengzhi. "Research on Railway Passenger Traffic Volume Forecasting Method." 2009 International Forum on Information Technology and Applications (2009): n. pag. Crossref. Web. https://doi.org/10.1109/ifita.2009.555

R. R. Chen, Yen-I Chiang, P. P. Chong, Y. H., Lin, & H. K. Chang. (2011). Rough set analysis on call center metrics. Applied Soft Computing, 11, 3804-3811. https://doi.org/10.1016/j.asoc.2011.02.013

O. Cosgun, Y. Ekinci, & S. Yanık. (2014). Fuzzy rule-based demand forecasting for dynamic pricing of a maritime company. Knowledge-Based Systems, 70, 88-96. https://doi.org/10.1016/j.knosys.2014.04.015

P. C. Faustino, A. C. Pinheiro, A. O. Carpinteiro, & I. Lima. (2011). Time series forecasting through rule-based models obtained via rough sets. Artif Intell Rev, 36, 299-310. https://doi.org/10.1007/s10462-011-9215-0

D. E. Gardner. (1981). Weight Factor Selection in Double Exponential Smoothing Enrolment forecasts. Research in Higher Education, 14(1), 49-56. https://doi.org/10.1007/BF00995369

C. Goh & R. Law. (2003). Incorporating the rough sets theory into travel demand analysis. Tourism Management, 24, 511-517. https://doi.org/10.1016/S0261-5177(03)00009-8

J. W. Grzymala-Busse. (1992). LERS-A system for learning from examples based on rough sets. In: R. Slowinski (Ed.). Intelligent Decision Support: Handbook of Application and Advances of Rough set theory. Kluwer Academic Publisher. Dordrecht, 3-18. https://doi.org/10.1007/978-94-015-7975-9_1

S. Hansun. (2016). A new approach of brown”s double exponential smoothing method in time series analysis. Balkan journal of Electrical & Computer Engineering, 4(2), 75-78. https://doi.org/10.17694/bajece.14351

C. C. Holt. (2004). Forecasting seasonal and trends by exponentially weighted moving averages. International Journal of Forecasting, 20, 5- 10. https://doi.org/10.1016/j.ijforecast.2003.09.015

C. I. Hsu & Y. H. Wen. (1998). Improved Grey prediction models for the trans-pacific air passenger market. Transportation Planning and Technology, 22, 87-107. https://doi.org/10.1080/03081069808717622

R. A. Ippolito. (1981). Estimating airline demand with quality of service variables. Journal of transport Economics and Policy, 7-15.

K. C. Kaemmerle. (1991). Estimating the demand for small community air service. Transportation Research 25A (2/3), 101-112. https://doi.org/10.1016/0191-2607(91)90129-E

Y. Kaya & M. Uyar. (2013). A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease. Applied soft computing, 13(8), 3429-3438. https://doi.org/10.1016/j.asoc.2013.03.008

M. Khashei, M. Bijari, & S. R. Hejazi. (2012). Combining seasonal ARIMA models with Computational intelligence techniques for time series forecasting. Soft Computing, 16, 1091-1105. https://doi.org/10.1007/s00500-012-0805-9

T. Korol. (2014). A fuzzy logic model for forecasting exchange rates. Knowledge-Based Systems, 67, 49-60. https://doi.org/10.1016/j.knosys.2014.06.009

B. Kostek. (1996). Rough Set and Fuzzy Set Methods Applied to Acoustical Analyses. Intelligent Automation & Soft Computing, 2(2), 147-160. https://doi.org/10.1080/10798587.1996.10750663

R. Law & N. Au. (1999). A neural network model to forecast Japanese demand for travel to Hong Kong. Tourism Management, 20(1), 89-97. https://doi.org/10.1016/S0261-5177(98)00094-6

R. Law & N. Au. (2000). Relationship modeling in tourism shopping: a decision rules induction approach. Tourism Management, 21, 241-249. https://doi.org/10.1016/S0261-5177(99)00056-4

R. Law. (2000). Back-propagation learning in improving the accuracy of neural network based tourism demand forecasting. Tourism Management, 21(4), 331-340. https://doi.org/10.1016/S0261-5177(99)00067-9

J. Li, F. Li, & G. Zhou. (2011). Travel Demand Prediction in Tangshan City of China Based on Rough Set. Springer-Verlag Berlin Heidelberg, 440-446. https://doi.org/10.1007/978-3-642-25255-6_56

G. Li, K. K. F. Wong, H. Song, & S. F. Witt. (2006). Tourism demand forecasting: A time-varying parameter error correction model. Journal of Travel Research, 45, 175-185. https://doi.org/10.1177/0047287506291596

J. Li, C. Huang, C. Mei, & Y. Yin. (2017). An intensive study on rule acquisition in formal decision contexts based on minimal closed label concept lattices. Intelligent Automation & Soft Computing, 23(3), 519-533. https://doi.org/10.1080/10798587.2016.1212509

P. Lingras. (1996). Evidential Comparisons Using Belief Functions, Rough Sets and No monotonic Preferences. Intelligent Automation & Soft Computing, 2(2), 203-210. https://doi.org/10.1080/10798587.1996.10750668

J. J. H. Liou, Y. C. Chuang, & C. C. Hsu. (2016). Improving airline service quality based on rough set theory and flow graphs. Journal of Industrial and Production Engineering, 33(2), 123-133. https://doi.org/10.1080/21681015.2015.1113571

S. Mahapatra & S. S. Sreekumar. (2010). Attribute selection in marketing: A rough set approach. IIMB Management Review, 22(1-2), 16-24. https://doi.org/10.1016/j.iimb.2010.03.001

A. Martin & F. Witt. (1989). Forecasting tourism demand: A comparison of the accuracy of several quantitative methods. International Journal of Forecasting, 5, 1-13. https://doi.org/10.1016/0169-2070(89)90059-9

K. Nam & T. Schaefer. (1995). Forecasting international airline passenger traffic using neural networks. Logistics and Transportation Review, 31(3), 239-252.

J. Y. Nancy, N. H. Khanna, & A. Kannan. (2017). A bio-statistical mining approach for classifying Multivariate clinical time series data observed at irregular intervals. Expert Systems with Applications, 78, 283-300. https://doi.org/10.1016/j.eswa.2017.01.056

J. Y. Nancy, N. H. Khanna, & A. Kannan. (2016). A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data. Applied Clinical Informatics, 7, 1-21. https://doi.org/10.4338/ACI-2015-08-RA-0102

H. Nassiri & A. Rezaei. (2012). Air itinerary choice in a low-frequency market: A decision rule approach. Journal of Air Transport Management, 18, 34-37. https://doi.org/10.1016/j.jairtraman.2011.08.001

S. I. Omurca. (2013).An intelligent supplier evaluation, selection and development system. Applied Soft Computing, 13, 690-697. https://doi.org/10.1016/j.asoc.2012.08.008

S. S. Pal & S. Kar. (2017).Time series forecasting using fuzzy transformation and neural network with back propagation learning, Journal of Intelligent & Fuzzy Systems, 33 (1), 467 - 477. https://doi.org/10.3233/JIFS-161767

Z. Pawlak. (1982). Rough sets. International Journal of Computer and Information Science, 11, 341-356. https://doi.org/10.1007/BF01001956

Z. Pawlak. (1991). Rough sets. A Theoretical Aspect of Reasoning about data. Kluwer Academic Publisher, Boston. https://doi.org/10.1007/978-94-011-3534-4

Z. Pawlak. (1996). Rough Sets Present State and Further Prospects. Intelligent Automation & Soft Computing, 2(2), 95-102. https://doi.org/10.1080/10798587.1996.10750659

Z. Pawlak & R. Slowinski. (1994). Rough set approach to multi-attribute decision analysis. European Journal of Operational Research, 72, 443-459. https://doi.org/10.1016/0377-2217(94)90415-4

Z. Pawlak & A. Skowron. (2007). Rudiments of rough sets. Information Sciences. An International Journal, 177(1), 3-27. https://doi.org/10.1016/j.ins.2006.06.003

B. Predki, S. K. M. Wong, J. Stefanowski, R. Susmaga, & Sz. Wilk, ROSE-software implementation of the rough set theory. In L. Pollkowski, A. Skowron (Eds.). (1998). Rough Sets and Current Trends in Computing. Lecture Notes in Artificial Intelligence. Berlin: Springer, 605-608.

M. G. Russon & N. F. Riley. (1993). Airport substitution in short haul model of air transportation. International Journal of Transportation Economics, 20 (2), 157-174.

M. Salamo & L. M. Sanchez. (2011). Rough set based approaches to feature selection for Case-Based Reasoning classifiers. Pattern Recognition Letters, 32, 280-292. https://doi.org/10.1016/j.patrec.2010.08.013

H. K. Sharma & S. Kar. (2018). Decision Making for Hotel Selection using Rough Set Theory: A case study of Indian Hotels. International Journal of Applied Engineering Research, 13(6), 3988-3998.

L. Shen & H. T. Loh. (2004). Applying rough sets to market timing decisions. Decision Support Systems, 37 (4), 583-597. https://doi.org/10.1016/S0167-9236(03)00089-7

B. Siregar, I. A. B. Butar, R. F. Rahmat, U. Andayani, & F. Fahmi. (2016). Comparison of exponential smoothing methods in forecasting palm oil real production. International Conference on Computing and Applied Informatics, Conf. Series 801.

F. E. H. Tay & L. Shen. (2003). Fault diagnosis based on Rough Set Theory. Engineering Applications of Artificial Intelligence, 16, 39-43. https://doi.org/10.1016/S0952-1976(03)00022-8

The OTEXTS (https://www.otexts.org/fpp/7/2).

S. Tsumoto & H. Tanaka. (1996). Machine Discovery of Functional Components of Proteins from Amino Acid Sequences Based on Rough Sets and Change of Representation. Intelligent Automation & Soft Computing, 2(2), 169-180. https://doi.org/10.1080/10798587.1996.10750665

L. Wu, S. Liu, & Y. Yang. (2016). Grey double exponential smoothing model and its application on pig price forecasting in China. Applied Soft Computing, 39, 117-123. https://doi.org/10.1016/j.asoc.2015.09.054

Xiaoya, He, and Jie Zhiben. "Research on Econometric Model for Domestic Tourism Income Based on Rough Set." Innovative Computing and Information (2011): 259-266. Crossref. Web. https://doi.org/10.1007/978-3-642-23998-4_37

L. Xu & S. Liu. (2012). Intelligent Pearl Disease Diagnosis Based on Rough Set - Neural Network Intelligent Automation & Soft Computing, 18(5), 469-476.

X. Xu, R. Law, W. Chen, & L. Tang. (2016). Forecasting tourism demand by extracting fuzzy Takagie Sugeno rules from trained SVMs. CAAI Transactions on Intelligence Technology, 1, 30-42. https://doi.org/10.1016/j.trit.2016.03.004

J. Yao & J. P. Herbert. (2009). Financial time-series analysis with rough sets. Applied Soft Computing, 9, 1000-1007. https://doi.org/10.1016/j.asoc.2009.01.003

J. Ye. (2014). Correlation coefficient of dual hesitant fuzzy sets and its application to multiple attribute decision making. Applied Mathematical Modelling, 38(2), 659-666. https://doi.org/10.1016/j.apm.2013.07.010

G. Yu & Z. Schwartz. (2006). Forecasting short time-series tourism demand with Artificial Intelligence models. Journal of Travel Research, 45, 194-203. https://doi.org/10.1177/0047287506291594

L. Zadeh. (1965). Fuzzy sets. Information and Control, 8, 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X

L. Y. Zhai, L. P. Khoo, & Z. W. Zhong. (2010). Towards a QFD-based expert system: A novel extension to fuzzy QFD methodology using rough set theory. Expert Systems with Applications, 37 (12), 8888-8896. https://doi.org/10.1016/j.eswa.2010.06.007

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present

SCImago Journal & Country Rank


CONTACT INFORMATION


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048
EMAIL: tsiepress@gmail.com
WEB: http://www.wacong.org/tsi/