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2019 | OriginalPaper | Buchkapitel

Fuzzy Time Series Model for Unequal Interval Length Using Genetic Algorithm

verfasst von : Shanoli Samui Pal, Samarjit Kar

Erschienen in: Information Technology and Applied Mathematics

Verlag: Springer Singapore

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Abstract

Time series analysis and forecasting depend on the observed historical data. As it deals with numerous number of information, imprecision occurs due to many reasons like truncation error, mechanical fault, noise. In such cases, fuzzy concept works well to handle impreciseness. Fuzzy time series forecasting methodology consists of defining universe of discourse (UOD), fuzzification of time series data points, assigning relationships between consecutive data points and defuzzification to get back the forecasting results in real domain. In this paper, UOD of a time series has been defined, unequal partitions of the UOD are established and then relationship among consecutive data points is evaluated to get the forecasting models. Genetic algorithm has been used in partitioning the UOD unequally and establishing the relationship. Forecasting model is applied on enrollment of University of Alabama, BSE sensex time series, and Shenzhen stock exchange data. The results are compared with conventional fuzzy time series models.

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Vorheriges Kapitel An Integrated Imperfect Production–Inventory Model with Optimal Vendor Investment and Backorder Price Discount

Nächstes Kapitel A Multi-item EPQ Model with Variable Demand in an Imperfect Production Process

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BSE Data Set. https://in.nance.yahoo.com/q/hp?=^BSESN

Chen, S.M., Chung, N.Y.: Forecasting enrollments using high-order fuzzy time series and genetic algorithms. Int. J. Intel. Syst. 21, 485–501 (2006). https://doi.org/10.1002/int.20145CrossRef

Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81, 311–319 (1996). https://doi.org/10.1016/0165-0114(95)00220-0CrossRef

Gangwar, S.S., Kumar, S.: Probabilistic and intuitionistic fuzzy sets based method for fuzzy time series forecasting. Cybernet. Syst. Int. J. 45, 349–361 (2014). https://doi.org/10.1080/01969722.2014.904135CrossRef

Halton, J.: Algorithm 247: radical-inverse quasi-random point sequence. Assoc. Comput. Mach. (ACM) 7, 701–702 (1964). https://doi.org/10.1145/355588.365104CrossRef

Hwang, J.R., Chen, S.M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Set Syst. 100, 217–228 (1998). https://doi.org/10.1016/S0165-0114(97)00121-8

Huarng, K.: Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst. 123, 387–394 (2001). https://doi.org/10.1016/S0165-0114(00)00057-9MathSciNetCrossRef

Jasim, H.T., Salim, A.G.J., Ibraheem, K.I.: A novel algorithm to forecast enrollment based on fuzzy time series. Appl. Appl. Math. (AAM) 7, 385–397 (2012)MathSciNetMATH

Joshi, B.P., Kumar, S.: Intuitionistic Fuzzy sets based method for fuzzy time series forecasting. Cybernet. Syst. Int. J. 43, 34–47 (2012). https://doi.org/10.1080/01969722.2012.637014CrossRef

10.

Lee, M.H., Efendi, R., Ismail, Z.: Modified weighted for enrollment forecasting based on Fuzzy time series. Matematika 25, 67–78 (2009). https://doi.org/10.11113/matematika.v25.n.260

11.

Lin, Y., Yang, Y.: Stock markets forecasting based on fuzzy time series model. In: IEEE International Conference Intelligent Computing and Intelligent Systems, pp. 782–786 (2009). https://doi.org/10.1109/ICICISYS.2009.5358026

12.

Pal, S.S., Pal, T., Kar, S.: An improvement in forecasting interval based Fuzzy time series. In: Conference Proceedings FUZZ IEEE 2014, Beijing, China, pp. 1390–1394 (2014). https://doi.org/10.1109/FUZZ-IEEE.2014.6891860

13.

Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series, Part I. Fuzzy Sets Syst. 54, 1–9 (1993). https://doi.org/10.1016/0165-0114(93)90355-LCrossRef

14.

Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series, Part II. Fuzzy Sets Syst. 62, 1–8 (1994). https://doi.org/10.1016/0165-0114(94)90067-1CrossRef

15.

Sakhuja, S., Jain, V., Kumar, S., Chandra, C., Ghildayal, S.K.: Genetic algorithm based fuzzy time series tourism demand forecast model. Indust. Manag. Data Syst. 116, 483–507 (2016). https://doi.org/10.1108/IMDS-05-2015-0165CrossRef

16.

Singh, P.: Rainfall and financial forecasting using fuzzy time series and neural networks based model. Int. J. Mach. Learn. Cybernet. (2016). https://doi.org/10.1007/s13042-016-0548-5

17.

Yu, H.K.: Weighted fuzzy time series models for Taiex forecasting. Phys. A 349, 609–624 (2005). https://doi.org/10.1016/j.physa.2004.11.006CrossRef

18.

Zhou, R., Zhan, Y., Cai, R., Tong, G.: A mean-variance hybrid-entropy model for portfoli selection with fuzzy returns. Entropy 17, 3319–3331 (2015). https://doi.org/10.3390/e17053319CrossRef

Titel: Fuzzy Time Series Model for Unequal Interval Length Using Genetic Algorithm
verfasst von: Shanoli Samui Pal
Samarjit Kar
Verlag: Springer Singapore
Buch: Information Technology and Applied Mathematics
Print ISBN: 978-981-10-7589-6

Electronic ISBN: 978-981-10-7590-2

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-981-10-7590-2_15

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