A novel data partitioning and rule selection technique for modeling high-order fuzzy time series

Applied Soft Computing Journal

Volumn 63, Issue , 2018, Pages 87-96

  Bose, Mahua ^a   Mali, Kalyani ^a  

^a UNIVERSITY OF KALYANI   (India)

Author keywords

Forecast; Interval; Membership; Rough set; Rule

Indexed keywords

FORECASTING; FUZZY LOGIC; ROUGH SET THEORY; TIME SERIES;

DATA PARTITIONING; FORECAST ACCURACY; FUZZY LOGICAL RELATIONSHIPS; FUZZY TIME SERIES; FUZZY TIME SERIES MODEL; INTERVAL; MEMBERSHIP; RULE;

DATA HANDLING;

EID: 85036462273     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2017.11.011     Document Type: Article

References (61)

1. Song, Q., Chissom, B., Fuzzy time series and its models. Fuzzy Sets Syst. 54 (1993), 269–277.
2. Song, Q., Chissom, B.S., Forecasting enrollments with fuzzy time series –part I. Fuzzy Sets Syst. 54 (1993), 1–9.
3. Song, Q., Chissom, B., Forecasting enrollments with fuzzy time series –part II. Fuzzy Sets Syst. 64 (1994), 1–8.
4. Chen, S.M., Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81 (1996), 311–319.
5. Chen, S.M., Forecasting enrollments based on high-order fuzzy time series. Cybern. Syst.: Int. J. 33 (2002), 1–16.
6. Sullivan, J., Woodall, W.H., A comparison of fuzzy forecasting and Markov modeling. Fuzzy Sets Syst. 64 (1994), 279–293.
7. Song, Q., A note on fuzzy time series model relation with sample autocorrelation functions. Cybern. Syst.: Int. J. 34 (2003), 93–107.
8. Lee, L.W., Wang, L.H., Chen, S.M., Leu, Y.H., Handling forecasting problems based on two-factors high-order fuzzy time series. IEEE Trans. Fuzzy Syst. 14:3 (2006), 468–477.
9. Yu, H.K., Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004), 609–624.
10. Cheng, C.H., Chang, J.R., Yeh, C.A., Entropy-based and trapezoid fuzzification-based fuzzy time series approaches for forecasting IT project cost. Technol. Forecast. Soc. Change 73 (2006), 524–542.
11. Chen, S.-M., Tanuwijaya, K., Fuzzy forecasting based on high-order fuzzy logical relationships and automatic clustering techniques. Expert Syst. Appl. 38 (2011), 15425–15437.
12. Chen, S.-M., Tanuwijaya, K., Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques. Expert Syst. Appl. 38 (2011), 10594–10605.
13. Izakian, H., Pedrycz, W., Jamal, I., Fuzzy clustering of time series data using dynamic time warping distance. Eng. Appl. Artif. Intell. 39 (2015), 235–244.
14. Singh, P., Borah, B., An efficient time series forecasting model based on fuzzy time series. Eng. Appl. Artif. Intell. 26 (2013), 2443–2457.
15. Singh, P., Borah, B., High-order fuzzy-neuro expert system for daily temperature. Forecast. Knowl.-Based Syst. 46 (2013), 12–21.
16. Wang, L., Liu, X., Pedrycz, W., Effective intervals determined by information granules to improve forecasting in fuzzy time series. Expert Syst. Appl. 40 (2013), 5673–5679.
17. Lu, W., Chen, X., Pedrycz, W., Liu, X., Yang, J., Using interval information granules to improve forecasting in fuzzy time series. Int. J. Approx Reasoning 57 (2015), 1–18.
18. Wang, W., Pedrycz, W., Liu, X., Time series long-term forecasting model based on information granules and fuzzy clustering. Eng. Appl. Artif. Intell. 41 (2015), 17–24.
19. Chen, M.Y., Chen, B.T., A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf. Sci. 294 (2015), 227–241.
20. Bose, M., Mali, K., High order time series forecasting using fuzzy discretization. Int. J. Fuzzy Syst. Appl., 5(4), 2016, 10.4018/IJFSA.2016100107.
21. Cheng, S.-H., Chen, S.-M., Jian, W.-S., Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Inf. Sci. 327 (2016), 272–287.
22. Chen, S.M., Kao, P.Y., TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines. Inf. Sci. 247 (2013), 62–71.
23. Singh, P., Borah, B., Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization. Int. J. Approximate Reasoning 55 (2014), 812–833.
24. Cai, Q., Zhang, D., Zheng, W., Leung, S.C.H., A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowl.-Based Syst. 74 (2015), 61–68.
25. Cai, Q.S., Zhang, D.F., Wu, B., Leung, C.H., A novel stock forecasting model based on fuzzy time series and genetic algorithm. Proc. Comput. Sci. 18 (2013), 1155–1162.
26. 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.
27. Aladag, C.H., Yolcu, U., Egrioglu, E., Bas, E., Fuzzy lagged variable selection in fuzzy time series with genetic algorithms. Appl. Soft Comput. 22 (2014), 465–473.
28. Ye, F., Zhang, L., Zhang, D., Fujita, H., Gong, Z., A novel forecasting method based on multi-order fuzzy time series and technical analysis. Inf. Sci. 367–368 (2016), 41–57.
29. Talarposhtia, F.M., Hossein, J.S., Rasul, E., Guimarãesc, F.G., Mahmud, M., Eslami, T., Stock market forecasting by using a hybrid model of exponential fuzzy time series. Int. J. Approximate Reasoning 70 (2016), 79–98.
30. Singh, S.R., A computational method of forecasting based on high-order fuzzy time series. Expert Syst. Appl. 36 (2009), 10551–10559.
31. Kumar, S., Gangwar, S.S., Partitions based computational method for high-order fuzzy time series. Forecast. Expert Syst. Appl. 39 (2012), 12158–12164.
32. Joshi, B.P., Kumar, S., A Computational method for fuzzy time series forecasting based on difference parameters. Int. J. Model. Simul. Sci. Comput., 4(1), 2013 (1250023-11250023-12).
33. Chen, S.M., Chu, H.P., Sheu, T.W., TAIEX forecasting using fuzzy time series and automatically generated weights of multiple factors. IEEE Trans. Syst. Man Cybernet. Part A Syst. Hum. 42:6 (2012), 1485–1495.
34. Chen, S.M., Chen, C.D., TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans. Fuzzy Syst. 19:1 (2011), 1–12.
35. Huarng, K., Yu, T.H.-K., A type-2 time series model for stock index forecasting. Physica A 353 (2005), 445–462.
36. Bajestani, N.S., Zare, A., Forecasting TAIEX using improved type-2 fuzzy time series. Expert Syst. Appl. 38 (2011), 5816–5821.
37. Chen, M.Y., A high-order fuzzy time series forecasting model for internet stock trading. Fut. Gener. Comput. Syst. 37 (2014), 461–467.
38. Singh, P., High-order fuzzy-neuro-entropy integration-based expert system for time series forecasting. Neural Comput. Appl., 2016, 1–18, 10.1007/s00521-016-2261-4.
39. Aladag, C.H., Yolcu, U., Egrioglu, E., A High order Fuzzy Time Series forecasting model based on adaptive expectation and artificial neural network. Math. Comput. Simul. 81 (2010), 875–882.
40. Yu, T.H.-K., Huarng, K., A neural network- based fuzzy time series model to improve forecasting. Expert Syst. Appl. 37 (2010), 3366–3372.
41. Yu, H.-K., Huarng, K., A bivariate fuzzy time series model to forecast TAIEX. Expert Syst. Appl. 34:4 (2008), 2945–2952.
42. Yu, H.-K., Huarng, K., Corrigendum to “A bivariate fuzzy time series model to forecast the TAIEX. Expert Syst. Appl., 37(7), 2010, 5529.
43. Wei, L.Y., Cheng, C.H., Wu, H.H., A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock. Appl. Soft Comput. 19 (2014), 86–92.
44. Aladag, C.H., Using multiplicative neuron model to establish fuzzy logic relationships. Expert Syst. Appl. 40:3 (2013), 850–853.
45. Sun, B., Guo, H., Karimi, H.R., Ge, Y., Xiong, S., Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series. Neurocomputing 151 (2015), 1528–1536.
46. Aladag, C.H., Yolcu, U., Egrioglu, E., Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks. Expert Syst. Appl. 40:3 (2013), 854–857.
47. Egrioglu, E., Aladag, C.H., Yolcu, U., Uslu, V.R., Erilli, N.A., Fuzzy time series forecasting method based on Gustafson–Kessel fuzzy clustering. Expert Syst. Appl. 38:8 (2011), 10355–10357.
48. Singh, P., Borah, B., An effective neural network and fuzzy time series-based hybridized model to handle forecasting problems of two factors. Knowl. Inf. Syst. 38:3 (2014), 669–690.
49. Singh, P., Rainfall and financial forecasting using fuzzy time series and neural networks based model. Int. J. Mach. Learn. Cyber, 2016, 1–16, 10.1007/s13042-016-0548-5.
50. Askari, S., Montazerin, N., A high-order multi-variable fuzzy time series forecasting algorithm based on fuzzy clustering. Expert Syst. Appl. 42:4 (2015), 2121–2135.
51. Cheng, C.H., Chen, T.L., Wei, L.Y., A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Inf. Sci. 180:9 (2010), 1610–1629.
52. Zadeh, L.A., Fuzzy set. Inf. Control 8 (1965), 338–353.
53. Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning. –Part I. Inf. Sci. 8 (1975), 199–249.
54. Pawlak, Z., Rough sets. Int. J. Comput. Inf. Sci. 11 (1982), 341–356.
55. Pawlak, Z., Rough sets and fuzzy sets. Fuzzy Sets Syst. 17 (1985), 99–102.
56. Dubois, D., Prade, H., Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17 (1990), 191–209.
57. Dubois, D., Prade, H., Putting rough sets and fuzzy sets together. Slowinski, R., (eds.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, 1992, Kluwer Academic Publishers, Boston, 203–222.
58. Maji, P., Pal, S.K., Rough set based generalized fuzzy c means algorithm and quantitative indices. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 37:6 (2007), 1529–1540.
59. Bouchachia, A., Lughofer, E., Sanchez, D., Editorial of the special issue: online fuzzy machine learning and data mining. Inf. Sci. 220 (2013), 1–4.
60. Bouchachia, A., Nedjah, N., Adaptive incremental learning in neural networks. Neurocomputing 74:11 (2011), 1783–1784.
61. Chen, M.Y., Chen, B.T., Online fuzzy time series analysis based on entropy discretization and a fast fourier transform. Appl. Soft Comput. 14 (2014), 156–166.