ClusFuDE: Forecasting low dimensional numerical data using an improved method based on automatic clustering, fuzzy relationships and differential evolution

Engineering Applications of Artificial Intelligence

Volumn 71, Issue , 2018, Pages 175-189

  Gupta, Charu ^a   Jain, Amita ^b   Tayal, Devendra Kumar ^c   Castillo, Oscar ^d  

^a BHAGWAN PARSHURAM INSTITUTE OF TECHNOLOGY   (India)

^b AMBEDKAR INSTITUTE OF ADVANCED COMMUNICATION TECHNOLOGIES AND RESEARCH   (India)

^c DELHI TECHNOLOGICAL UNIVERSITY   (India)

^d TIJUANA INSTITUTE OF TECHNOLOGY   (Mexico)

Author keywords

Automatic clustering; Differential evolution; Evolutionary computation; Forecasting; Fuzzy logical relationships; University enrollment

Indexed keywords

EVOLUTIONARY ALGORITHMS; FORECASTING; FUZZY LOGIC; ITERATIVE METHODS; MEAN SQUARE ERROR; OPTIMIZATION;

AUTOMATIC CLUSTERING; DIFFERENTIAL EVOLUTION; DIFFERENTIAL EVOLUTION METHOD; FUZZY LOGICAL RELATIONSHIPS; ITERATIVE MODIFICATIONS; MEAN ABSOLUTE PERCENTAGE ERROR; MEAN SQUARE ERROR (MSE); UNIVERSITY ENROLLMENT;

NUMERICAL METHODS;

EID: 85043460854     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2018.02.015     Document Type: Article

References (65)

1. Aladag, C.H., Yolcu, U., Egrioglu, E., Dalar, A.Z., A new time invariant fuzzy time series forecasting method based on particle swarm optimization. Appl. Soft Comput. 12:10 (2012), 3291–3299.
2. Aydin, I., Karakose, M., Akin, E., The prediction algorithm based on fuzzy logic using time series data mining model. World Acad. Sci. Eng. Technol., 51, 2009.
3. Bas, E., Uslu, V.R., Yolcu, U., Egrioglu, E., A modified genetic algorithm for forecasting fuzzy time series. Appl. Intell. 41:2 (2014), 453–463.
4. Bisht, K., Kumar, S., Fuzzy time series forecasting method based on hesitant fuzzy sets. Expert Syst. Appl. 64 (2016), 557–568.
5. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V., Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10:6 (2006), 646–657.
6. Chen, S.M., Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems 81:3 (1996), 311–319.
7. Chen, S.M., Forecasting enrollments based on high-order fuzzy time series. Cybern. Syst. 33:1 (2002), 1–16.
8. Chen, S.M., Chen, C.D., Handling forecasting problems based on high-order fuzzy logical relationships. Expert Syst. Appl. 38:4 (2010), 3857–3864.
9. Chen, S.M., Chen, C.D., Handling forecasting problems based on high-order fuzzy logical relationships. Expert Syst. Appl. 38:4 (2010), 3857–3864.
10. Chen, S.M., Chung, N., Forecasting enrollments using high order fuzzy time series and genetic algorithms. Int. J. Intell. Syst. 21:5 (2006), 485–501.
11. Chen, S.M., Hsiao, H.R., A new method to estimate null values in relational database systems based on automatic clustering techniques. Inform. Sci. 169:1–2 (2005), 47–69.
12. Chen, S.M., Hsu, C.C., A new method to forecast enrollments using fuzzy time series. Int. J. Appl. Sci. Eng. 2:3 (2004), 234–244.
13. Chen, S.M., Hsu, C.C., A new approach for handling forecasting problems using high order fuzzy time series. Intell. Autom. Soft Comput. 14:1 (2008), 29–43.
14. Chen, S.M., Hwang, J.R., Temperature prediction using fuzzy time series. IEEE Trans. Syst. Man Cybern. B 30:2 (2000), 263–275.
15. Chen, S.M., Kau, P.Y., TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines. Inform. Sci. 247 (2013), 62–71.
16. Chen, S.M., Manalu, G.M.T., Pan, J.S., Liu, H.C., Fuzzy forecasting based on two factor second order fuzzy trend logical relationship group and particle swarm optimization. IEEE Trans. Cybern. 43:3 (2013), 1102–1117.
17. Chen, S.M., Wang, N.Y., Fuzzy forecasting based on fuzzy-trend logical relationship groups. IEEE Trans. Syst. Man Cybern. B, 40(5), 2010.
18. Chen, S.M., Wang, N.Y., Pan, J.S., Forecasting enrollments using automatic clustering techniques and fuzzy logical relationships. Expert Syst. Appl., 36(8), 2009.
19. Cheng, C.H., Wang, J.W., Li, C.H., Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix. Expert Syst. Appl. 34 (2008), 2568–2575.
20. Das, S., Abraham, A., Konar, A., Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man Cybern. A, 38(1), 2008.
21. Das, S., Abraham, A., Konar, A., Particle swarm optimization and differential evolution algorithms: technical analysis, applications and hybridization perspective. Stud. Comput. Intell. 116 (2008), 1–38.
22. Das, S., Konar, A., Chakraborty, U.K., 2005. Two improved differential evolution schemes for faster global search. In Proceedings of ACM-SIGEVO GECCO, Washington, DC, pp. 991–998.
23. Feoktistov, V., Differential Evolution: In Search of Solutions. 2006, Springer.
24. Hadavandi, E., Shavandi, H., Ghanbari, A., Abbasian-Naghneh, S., Developing a hybrid artificial intelligence model for outpatient visits forecasting in hospitals. Appl. Soft Comput. 12:2 (2012), 700–711.
25. Holland, J.H., Adaptation in Natural and Artificial Systems. 1975, University of Michigan Press, Ann Arbor, MI.
26. Huang, Y.L., Horng, S.J., Kao, T.W., Run, R.S., Lai, J.L., Chen, R.J., et al. An improved forecasting model based on the weighted fuzzy relationship matrix combined with a pso adaptation for enrollments. Int. J. Innov. Comput. Inf. Control 7:7A (2011), 4027–4046.
27. Huarng, K., Effective length of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems 123:3 (2001), 387–394.
28. Huarng, K., Yu, H.K., Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Trans. Syst. Man Cybern. B 36:2 (2006), 328–340.
29. Hwang, J.R., Chen, S.M., Lee, C.H., Handling forecasting problems using fuzzy time series. Fuzzy Sets and Systems 100:2 (1998), 217–228.
30. Jain, A.K., Murty, M.N., Flynn, P.J., Data clustering: A review. ACM Comput. Surv., 31(3), 1999.
31. Jasim, H.T., Salim, A.G.J., A novel algorithm to forecast enrollment based on fuzzy time series. Int. J. Appl. Appl. Math. 7:1 (2012), 385–397.
32. Jiang, P., Dong, Q., Li, P., Lian, L., A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction. Appl. Soft Comput. 55 (2017), 44–62.
33. Katari, V., Satapathy, S.C., Murthy, J.V.R, Reddy, P.V.G.D., Hybridized improved genetic algorithm with variable length chromosome for image clustering. Int. J. Comput. Sci. Netw. Secur. 7:11 (2007), 121–131.
34. Lahmeyer, J.J., 2003. India, Historical Demographical data of the whole country. [Online] Available at: http://www.populstat.info/. [Accessed August 2016].
35. Lai, K.K., Yu, L., Wang, S., Huang, W., Hybridizing exponential smoothing and neural network for financial time series predication. Comput. Sci., 2006, 493–500.
36. Lee, L.W., Wang, L.H., Chen, S.M., Leu, Y.H., 2006. A new method for handing forecasting problems based on two-factors high-order fuzzy time series. In: Proceedings of 9th International Conference on the Application of Artificial Intelligence, Taipei, Taiwan.
37. Meng, H., Wang, G., Zhang, X., Deng, W., Yan, H., Jia, R., Water quality prediction based on a novel fuzzy time series model and automatic clustering technique. Ciucci, D., Wang, G., Mitra, S., Wu, W.Z., (eds.) Rough Sets and Knowledge Technology Lecture Notes in Computer Science, vol. 9436, 2015, Springer.
38. Mitchell, R.J., 2003. Forecasting electricity demand using clustering. In: Proceedings of the 21st IASTED international conference on applied informatics, Innsbruck, Austria, pp. 225–230.
39. Paterlini, S., Krink, T., Differential evolution and particle swarm optimization. Comput. Statist. Data Anal., 2006, 1220–1247.
40. Paterlini, S., Minerva, T., Evolutionary approaches for cluster analysis. Bonarini, A., Masulli, F., Pasi, G., (eds.) Soft Computing Applications, 2003, Springer-Verlag, Berlin, Germany, 167–178.
41. Qin, A.K., Suganthan, P.N., Self- adaptive differential evolution algorithm for numerical optimization. Evolutionary Computation, 2005, IEEE, 1785–1791.
42. Qiu, W., Zhang, P., Wang, Y., 2015. Fuzzy time series forecasting model based on automatic clustering techniques and generalized fuzzy logical relationship. Mathematical Problems in Engineering.
43. Radmehr, F., Gharneh, N.S., Forecasting method based on high order fuzzy time series and simulated annealing technique: case study. South Afr. J. Ind. Eng. 23:2 (2012), 176–190.
44. Rajaram, S., Sakthisree, P., Forecasting enrollments using interval length, Weightage factor and fuzzy logical relationships. Int. J. Stat. Syst. 5:2 (2010), 173–182.
45. Ross, T.J., Fuzzy Logic with Engineering Applications. 1995, McGraw-Hill, New York.
46. Sakhuja, S., Jain, V., Kumar, S., Chandra, C., Ghildayal, S.K., Genetic algorithm based fuzzy time series tourism demand forecast model. Ind. Manag. Data Syst. 116:3 (2016), 483–507.
47. Shahraki, A., Ebrahimi, S.B., A new approach for forecasting enrollments using harmony search algorithm. J. Intell. Fuzzy. Syst. 28:1 (2015), 279–290.
48. Singh, S.R., A simple method of forecasting based on fuzzy time series. Appl. Math. Comput. 186 (2007), 330–339.
49. Singh, S.R., A computational method of forecasting based on fuzzy time series. Math. Comput. Simulation 79 (2008), 539–554.
50. Song, Q., Chissom, B.S., Forecasting enrollments with fuzzy time series—Part I. Fuzzy Sets and Systems 54:1 (1993), 1–9.
51. Storn, R., Price, K., A simple and efficient heuristic for global optimization over continuous space. J. Global Optim. 34 (1997), 1–359.
52. Sullivan, J., Woodall, W.H., A comparison of fuzzy forecasting and Markov modeling. Fuzzy Sets and Systems 64:3 (1994), 279–293.
53. Tanuwijaya, K., Chen, S.M., 2009. A new method to forecast enrollments using fuzzy time series and clustering techniques. In: proceedings of 8th International Conference on Machine learning and Cybernetics, Baoding, China.
54. Tayal, D., Ansari, G., Sonawani, S., Gupta, C., Fuzzy time series forecasting of low dimensional numerical data. Int. Jr. Eng. Res. Appl. 2:1 (2011), 132–135.
55. Tvrdik, J., Differential evolution with competitive setting of control parameters. Task Quart. 11:1–2 (2007), 169–179.
56. Vesterstrom, J., Thomsen, R., A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. Evolutionary Computation, 2004. CEC2004. Congress on, vol. 2, 2004, IEEE, 1980–1987.
57. Wazan, H.A., Ibraheem, K., Salim, A.J., A hybrid algorithm to forecast enrolment based on genetic algorithms and fuzzy time series. Int. Arab J. Inf. Technol. 11:3 (2014), 250–257.
58. Wu, W., Fuzzy reasoning and fuzzy relation equations. Fuzzy Sets Syst. 20 (1986), 67–68.
59. Xihao, S., Yimin, L., Average-based fuzzy time series models for forecasting shanghai compound index. World J. Model. Simul. 4:2 (2008), 104–111.
60. Ye, F., Zhang, L., Zhang, D., Fujita, H., Gong, Z., A novel forecasting method based on multi-order fuzzy time series and technical analysis. Inform. Sci. 367 (2016), 41–57.
61. Yu, H.K., Weighted fuzzy time series models for TAIEX forecasting. Physica A 349 (2005), 609–624.
62. Zadeh, L.A., Fuzzy sets. Inf. Control 8 (1965), 338–353.
63. Zadeh, L.A., The concept of linguistic variable and its application to an approximate reasoning. Inform. Sci. 8 (1975), 199–249.
64. Zaharie, D., 2007. A comparative analysis of crossover variants in differential evolution. In: Proceedings of the International Multi-conference on Computer Science and Information Technology, pp. 171–181.
65. Zarandi, M.H.F., Molladavoudi, A., Beigi Ali, M.H., A new method for temperature prediction and the TAIFEX Forecasting based on fuzzy logical relationship and double interval division. Ind. Eng. Eng. Manag. IEEM 1 (2009), 1543–1547.