Enhanced accuracy of fuzzy time series model using ordered weighted aggregation

Applied Soft Computing Journal

Volumn 48, Issue , 2016, Pages 265-280

  Garg, Bindu ^a   Garg, Rohit ^b  

^a BHARATI VIDYAPEETH S COLLEGE OF ENGINEERING   (India)

^b TATA CONSULTANCY SERVICES   (India)

Author keywords

Fuzzy logic relation; Fuzzy time series; Ordered weighted aggregation (OWA)

Indexed keywords

FORECASTING; FUZZY LOGIC; MEAN SQUARE ERROR; TIME SERIES; WEIGHING;

AVERAGE FORECASTING ERROR; COMPARATIVE STUDIES; FORECASTING MODELING; FUZZY ENVIRONMENTS; FUZZY TIME SERIES; FUZZY TIME SERIES MODEL; HISTORICAL OBSERVATION; ORDERED WEIGHTED AGGREGATIONS;

TIME SERIES ANALYSIS;

EID: 84979530192     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2016.07.002     Document Type: Article

References (52)

1. [1] Garg, B., Beg, M.M.S., Ansari, A.Q., Imran, B.M., Inferential historical survey of time series predication using artificial neural network. Proc. 3rd International Conference on Data Management, Ghaziabad, India, 2010, 158–172.
2. [2] Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning-II. Inf. Sci. 8:4 (1975), 301–357.
3. [3] Zadeh, L.A., Fuzzy sets. Inf. Control 8 (1965), 333–353 (Pg/).
4. [4] Song, Q., Chissom, B.S., Fuzzy time series and its models. Fuzzy Sets Syst. 54 (1993), 269–277 (Pg).
5. [5] Song, Q., Chissom, B.S., Forecasting enrollments with fuzzy time series: part II. Fuzzy Sets Syst. 62 (1994), 1–8.
6. [6] Song, Q., A note on fuzzy time series model selection with sample autocorrelation functions, an international. J. Cybern. Syst. 34 (2003), 93–107 (Pg).
7. [7] Che, S.M., Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81 (1996), 311–319.
8. [8] Chen, S.M., Forecasting enrollments based on high order fuzzy time series. Int. J. Cybern. Syst. 33 (2002), 1–16.
9. [9] Huarng, K., Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst. 12 (2001), 387–394.
10. [10] Huarng, K., Heuristic models of fuzzy time series for forecasting. Fuzzy Sets Syst. 123 (2002), 369–386.
11. [11] Hwang, J.R., Chen, S.M., Lee, C.H., Handling forecasting problems using fuzzy time series. Fuzzy Sets Syst. 100 (1998), 217–228.
12. [12] Lee, L.W., Wang, L.W., Chen, S.M., Handling forecasting problems based on two-Factors high-Order time series. IEEE Trans. Fuzzy Syst. 14 (2006), 468–477.
13. [13] Li, H., Kozma, R., A dynamic neural network method for time series prediction using the KIII model. Proceedings of the 2003 International Joint Conference on Neural Networks, 2003, 347–352.
14. [14] Singh, S.R., A robust method of forecasting based on fuzzy time series. International J. Appl. Math. Comput. 188 (2007), 472–484.
15. [15] Singh, S.R., A computational method of forecasting based on fuzzy time series. Int. J. Math. Comput. Simul. 79 (2008), 539–554.
16. [16] Lee, C.H.L., Lin, A., Chen, W.S., Pattern discovery of fuzzy time series for financial prediction. IEEE Trans. Knowl. Data Eng. 18 (2006), 613–625.
17. [17] Huanrg, K.H., Yu, T.H.K., Hsu, Y.W., A multivariate heuristic model for forecasting. IEEE Trans. Syst. Man Cybern. 37 (2007), 836–846.
18. [18] Yolcu, U., Egrioglu, E., Vedide, R., Uslu, R., Basaran, M.A., Aladag, C.H., A new approach for determining the length of intervals for fuzzy time series. Appl. Soft Comput. 9 (2009), 647–651.
19. [19] Qiu, W., Liu, X., Li, H., A generalized method for forecasting based on fuzzy time series. Expert Syst. Appl. 38 (2011), 10446–10453.
20. [20] Jilani, T.A., Burney, S.M.A., Ardil, C., Multivariate high order fuzzy time series forecasting for car road accidents. Int. J. Comput. Intell. 4 (2007), 15–20.
21. [21] Garg, B., Beg, M.M.S., Ansari, A.Q., Imran, B.M., Fuzzy Time Series Prediction Model, Communications in Computer and Information Science, 141, 2011, Springer−Verlag, Berlin Heidelberg, 126–137 (ISBN978-3-642-19423-8).
22. [22] Garg, B., Beg, M.M.S., Ansari, A.Q., Imran, B.M., Soft Computing Model to Predict Average Length of Stay of Patient, Communications in Computer and Information Science, 141, 2011, Springer Verlag, Berlin Heidelberg, 221–232 (ISBN978-3-642-19423-8).
23. [23] Garg, B., Beg, M.M.S., Ansari, A.Q., Enhanced accuracy of fuzzy time series predictor using genetic algorithm. Third IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC2011), Spain, 2011, 273–278.
24. [24] Garg, B., Beg, M.M.S., Ansari, A.Q., Employing genetic algorithm to optimize OWA-Fuzzy forecasting model. Third IEEE World Congress on Nature and Biologically Inspired Computing (NaBIC2011), Spain, 2011, 285–290.
25. [25] Garg, B., Beg, M.M.S., Ansari, A.Q., A new computational fuzzy time series model to forecast number of outpatient visits. Proc. 31 St Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS 2012), University of California at Berkeley, USA August 6–8, 2012, 1–6.
26. [26] Yu, H.K., Weighted fuzzy time series models for TAIEX forecasting. Physica 349 (2005), 609–624.
27. [27] Cheng, Ching-Hsue, Wang, Jia-Wen, Li, Chen-Hsun, Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-Transitional matrix. Expert Syst. Appl. 34 (2008), 2568–2575.
28. [28] Knod, E.M., Schonberger, R.J., Operations Management-Meeting Customers’ Demands. 7th ed., 2001, McGraw Hill international.
29. [29] Goldman, W., McCulloch, J., Stur, M.R., Costs and use of mental health services before and after managed care. Health Aff. (Millwood) 17:2 (1998), 40–52.
30. [30] Yager, R.R., On ordered weighted averaging aggregation operators in multi criteria decision making. IEEE Trans. Syst. Man Cybern. SMC-18:1 (1988), 183–190.
31. [31] Yager, R.R., Time series smoothing and OWA aggregation. IEEE Trans. Fuzzy Syst., 16(No-4), 2008.
32. [32] Beg, M.M.S., User feedback based enhancement in web search quality. Int. J. Inf. Sci., Elsevier 170 (2005), 153–172.
33. [33] Sadiq, R., Tesfamariam, S., Developing environmental indices using fuzzy numbers ordered weighted averaging (FN-OWA) operators. Int. J. Stochastic Environ. Res. Risk Manage. 22:No. 1 (2008), 494–505.
34. [34] Cheng, C.H., Huang, S.F., Forecasting of ozone concentration using frequency MA-OWA model. IEEE International Conference on System, Man and Cybernetics, San Antonio, TX, USA, 2009.
35. [35] Box, G.E.P., Jenkins, G.M., Reinsel, G., Time Series Analysis, Forecasting, and Control. 1994, Holden-Day, Inc., San Francisco Calif (ISBN-13: 978-0130607744).
36. [36] Brockwell, P.J., Davis, R.A., Introduction to Time Series & Forecasting. 2002, Springer-Verlag (ISBN 0-387-95351-5).
37. [37] Yager, R.R., Quantifier weighted averaging using OWA operators. Int. J. Intell. Syst. 11 (1996), 49–73.
38. [38] Yager, R.R., Filev, D.P., Parameterized andlike and orlike OWA operators. Int. J. Gen. Syst. 22 (1996), 297–316.
39. [39] Tsaur, R.C., Yang, J.C.O., Wang, H.F., Fuzzy relation analysis in fuzzy time series model. Comput. Math. Appl. 49 (2005), 539–548 (Pg).
40. [40] Pedrycz, W., Why triangular membership function. IEEE Trans. Fuzzy Set Syst. 64 (1994), 21–30.
41. [41] Box, G.P., Jenkins, G.M., Time Series Analysis: Forecasting and Control. 1976, Holden-day Inc., San Francisco CA.
42. [42] Chen, M.-Y., Chen, B.-T., Online fuzzy time series analysis based on entropy discretization and a fast fourier transform. Appl. Soft Comput. 14 (2013), 156–166.
43. [43] Cai, Q., Zhang, D., Zheng, W., Leung, S.C.H., A new wieghted fuzzy time series forecasting model combined with ant colony optimization and auto-regression. Knowl. Based Syst. 74 (2015), 61–68.
44. [44] Chen, S.M., Chen, C.D., TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans. Fuzzy Syst. 19 (2011), 1–12.
45. [45] Chen, Mu-Yen, A high-order fuzzy time series forecasting model for internet stock trading. Future Gener. Comput. Syst. 37 (2014), 461–467.
46. [46] 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.
47. [47] Fullerand, R., Majlender, P., An analytic approach for obtaining maximal entropy OWA operator weights. Fuzzy Sets Syst., 124, 2001, 53 (Pg57).
48. [48] Ahn, S.B., On the Properties of OWA operator weights function with constant level of Orness. IEEE Trans. Fuzzy Syst. 14 (2006), 511–515.
49. [49] Cheng, J.R., Cheng, C.H., MCDM aggregation model by ME-OWA and ME-OWGA operators, international journal of uncertainty. Fuzziness Knowl. Based Syst. 14 (2006), 421–443.
50. [50] Cheng, C.H., Wang, J.W., Wu, M.C., OWA weighed based clustering method for classification problems. Expert Syst. Appl. 36 (2009), 4988–4995.
51. [51] Cheng, C.H., Wei, L.Y., Liu, J.W., Chen, T.L., OWA based ANFIS model for TAIEX forecasting. Econ. Model. 30 (2013), 442–448.
52. [52] Chang, J.R., A weighted fuzzy time series model on adoptive OWA operators. International Conference on Uncertainty Reasoning and Knowledge Engineering 2 (2011), 94–97 (ISBN:978-1-4244-9984-7).