메뉴 건너뛰기




Volumn 118, Issue , 2017, Pages 204-216

Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors

Author keywords

Fuzzy time series forecasting; Particle swarm optimization; Two factors second order fuzzy logical relationships; Two factors second order fuzzy trend logical relationship groups

Indexed keywords

FORECASTING; PARTICLE SWARM OPTIMIZATION (PSO); TIME SERIES;

EID: 85009230876     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2016.11.019     Document Type: Article
Times cited : (118)

References (46)
  • 1
    • 78049528355 scopus 로고    scopus 로고
    • A heuristic time-invariant model for fuzzy time series forecasting
    • [1] Bai, E., Wong, W.K., Chu, W.C., Xia, M., Pan, F., A heuristic time-invariant model for fuzzy time series forecasting. Expert Syst. Appl. 38:3 (2011), 2701–2707.
    • (2011) Expert Syst. Appl. , vol.38 , Issue.3 , pp. 2701-2707
    • Bai, E.1    Wong, W.K.2    Chu, W.C.3    Xia, M.4    Pan, F.5
  • 2
    • 85009193283 scopus 로고    scopus 로고
    • Particle swarm optimization: a tutorial, [Online]. Available:,
    • [2] J. Blondin, Particle swarm optimization: a tutorial, [Online]. Available: www.cs.armstrong.edu/saad/csci8100/pso_tutorial.pdf, 2009.
    • (2009)
    • Blondin, J.1
  • 3
    • 84926191810 scopus 로고    scopus 로고
    • A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression
    • [3] Cai, Q., Zhang, D.F., 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.
    • (2015) Knowl.-Based Syst. , vol.74 , pp. 61-68
    • Cai, Q.1    Zhang, D.F.2    Zheng, W.3    Leung, S.C.H.4
  • 4
    • 0001073047 scopus 로고    scopus 로고
    • Forecasting enrollments based on fuzzy time series
    • [4] Chen, S.M., Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81:3 (1996), 311–319.
    • (1996) Fuzzy Sets Syst. , vol.81 , Issue.3 , pp. 311-319
    • Chen, S.M.1
  • 5
    • 77957661892 scopus 로고    scopus 로고
    • Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy interpolation techniques
    • [5] Chen, S.M., Chang, Y.C., Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy interpolation techniques. Inf. Sci. 180:24 (2010), 4772–4783.
    • (2010) Inf. Sci. , vol.180 , Issue.24 , pp. 4772-4783
    • Chen, S.M.1    Chang, Y.C.2
  • 6
    • 79551645335 scopus 로고    scopus 로고
    • TAIEX forecasting based on fuzzy time series and fuzzy variation groups
    • [6] 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.
    • (2011) IEEE Trans. Fuzzy Syst. , vol.19 , Issue.1 , pp. 1-12
    • Chen, S.M.1    Chen, C.D.2
  • 7
    • 85027921885 scopus 로고    scopus 로고
    • Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships
    • [7] Chen, S.M., Chen, S.W., Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships. IEEE Trans. Cybern. 45:3 (2015), 405–417.
    • (2015) IEEE Trans. Cybern. , vol.45 , Issue.3 , pp. 405-417
    • Chen, S.M.1    Chen, S.W.2
  • 8
    • 84880922220 scopus 로고    scopus 로고
    • TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines
    • [8] 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.
    • (2013) Inf. Sci. , vol.247 , pp. 62-71
    • Chen, S.M.1    Kao, P.Y.2
  • 9
    • 84890427180 scopus 로고    scopus 로고
    • Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques
    • [9] Chen, S.M., Manalu, G.M.T., Pan, J.S., Liu, H.C., Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Trans. Cybern. 43:3 (2013), 1102–1117.
    • (2013) IEEE Trans. Cybern. , vol.43 , Issue.3 , pp. 1102-1117
    • Chen, S.M.1    Manalu, G.M.T.2    Pan, J.S.3    Liu, H.C.4
  • 11
    • 84866144636 scopus 로고    scopus 로고
    • Experimental study on boundary constraints handling in particle swarm optimization: from population diversity perspective
    • [11] Cheng, S., Shi, Y., Qin, Q., Experimental study on boundary constraints handling in particle swarm optimization: from population diversity perspective. Int. J. Swarm Intell. Res. 2:3 (2011), 43–69.
    • (2011) Int. J. Swarm Intell. Res. , vol.2 , Issue.3 , pp. 43-69
    • Cheng, S.1    Shi, Y.2    Qin, Q.3
  • 12
    • 84944210856 scopus 로고    scopus 로고
    • Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures
    • [12] 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.
    • (2016) Inf. Sci. , vol.327 , pp. 272-287
    • Cheng, S.H.1    Chen, S.M.2    Jian, W.S.3
  • 14
    • 0035502063 scopus 로고    scopus 로고
    • Effective lengths of intervals to improve forecasting in fuzzy time series
    • [14] Huarng, K., Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets Syst. 123:3 (2001), 387–394.
    • (2001) Fuzzy Sets Syst. , vol.123 , Issue.3 , pp. 387-394
    • Huarng, K.1
  • 15
    • 33644994175 scopus 로고    scopus 로고
    • Ratio-based lengths of intervals to improve fuzzy time series forecasting
    • [15] Huarng, K., Yu, T.H.K., Ratio-based lengths of intervals to improve fuzzy time series forecasting. IEEE Trans. Syst. Man Cybern. Part B 36:2 (2006), 328–340.
    • (2006) IEEE Trans. Syst. Man Cybern. Part B , vol.36 , Issue.2 , pp. 328-340
    • Huarng, K.1    Yu, T.H.K.2
  • 16
    • 33644868985 scopus 로고    scopus 로고
    • The application of neural networks to forecast fuzzy time series
    • [16] Huarng, K., Yu, T.H.K., The application of neural networks to forecast fuzzy time series. Physica A 363:2 (2006), 481–491.
    • (2006) Physica A , vol.363 , Issue.2 , pp. 481-491
    • Huarng, K.1    Yu, T.H.K.2
  • 17
    • 34547132315 scopus 로고    scopus 로고
    • A multivariate heuristic model for fuzzy time-series forecasting
    • [17] Huarng, K., Yu, T.H.K., Hsu, Y.W., A multivariate heuristic model for fuzzy time-series forecasting. IEEE Trans. Syst. Man Cybern.-Part B 37:4 (2007), 836–846.
    • (2007) IEEE Trans. Syst. Man Cybern.-Part B , vol.37 , Issue.4 , pp. 836-846
    • Huarng, K.1    Yu, T.H.K.2    Hsu, Y.W.3
  • 19
    • 58349092063 scopus 로고    scopus 로고
    • An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization
    • [19] Kuo, I.H., Horng, S.J., Kao, T.W., Lin, T.L., Lee, C.L., Pan, Y., An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Syst. Appl. 36:3 (2009), 6108–6117.
    • (2009) Expert Syst. Appl. , vol.36 , Issue.3 , pp. 6108-6117
    • Kuo, I.H.1    Horng, S.J.2    Kao, T.W.3    Lin, T.L.4    Lee, C.L.5    Pan, Y.6
  • 20
    • 33745177648 scopus 로고    scopus 로고
    • Handling forecasting problems based on two-factors high-order fuzzy time series
    • [20] 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.
    • (2006) IEEE Trans. Fuzzy Syst. , vol.14 , Issue.3 , pp. 468-477
    • Lee, L.W.1    Wang, L.H.2    Chen, S.M.3    Leu, Y.H.4
  • 21
    • 33847636706 scopus 로고    scopus 로고
    • Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms
    • [21] Lee, L.W., Wang, L.H., Chen, S.M., Temperature prediction and TAIFEX forecasting based on fuzzy logical relationships and genetic algorithms. Expert Syst. Appl. 33:3 (2007), 539–550.
    • (2007) Expert Syst. Appl. , vol.33 , Issue.3 , pp. 539-550
    • Lee, L.W.1    Wang, L.H.2    Chen, S.M.3
  • 22
    • 34248566920 scopus 로고    scopus 로고
    • Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques
    • [22] Lee, L.W., Wang, L.H., Chen, S.M., Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques. Expert Syst. Appl. 34:1 (2008), 328–336.
    • (2008) Expert Syst. Appl. , vol.34 , Issue.1 , pp. 328-336
    • Lee, L.W.1    Wang, L.H.2    Chen, S.M.3
  • 23
    • 60249088909 scopus 로고    scopus 로고
    • A distance-based fuzzy time series model for exchange rates forecasting
    • [23] Leu, Y., Lee, C.P., Jou, Y.Z., A distance-based fuzzy time series model for exchange rates forecasting. Expert Syst. Appl. 36:4 (2009), 8107–8114.
    • (2009) Expert Syst. Appl. , vol.36 , Issue.4 , pp. 8107-8114
    • Leu, Y.1    Lee, C.P.2    Jou, Y.Z.3
  • 24
    • 72949092681 scopus 로고    scopus 로고
    • Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX
    • [24] Liu, J.W., Chen, T.L., Cheng, C.H., Chen, Y.H., Adaptive-expectation based multi-attribute FTS model for forecasting TAIEX. Comput. Math. Appl. 59:2 (2010), 795–802.
    • (2010) Comput. Math. Appl. , vol.59 , Issue.2 , pp. 795-802
    • Liu, J.W.1    Chen, T.L.2    Cheng, C.H.3    Chen, Y.H.4
  • 25
    • 84975162027 scopus 로고    scopus 로고
    • Evolving granular analytics for interval time series forecasting
    • [25] Maciel, L., Ballini, R., Gomide, F., Evolving granular analytics for interval time series forecasting. Granular Comput., 1(4), 2016.
    • (2016) Granular Comput. , vol.1 , Issue.4
    • Maciel, L.1    Ballini, R.2    Gomide, F.3
  • 28
    • 84894493014 scopus 로고    scopus 로고
    • Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization
    • [28] Singh, P., Borah, B., Forecasting stock index price based on M-factors fuzzy time series and particle swarm optimization. Int. J. Approximate Reasoning 55:3 (2014), 812–833.
    • (2014) Int. J. Approximate Reasoning , vol.55 , Issue.3 , pp. 812-833
    • Singh, P.1    Borah, B.2
  • 29
    • 38149147511 scopus 로고
    • Forecasting enrollments with fuzzy time series-part I
    • [29] Song, Q., Chissom, B.S., Forecasting enrollments with fuzzy time series-part I. Fuzzy Sets Syst. 54:1 (1993), 1–9.
    • (1993) Fuzzy Sets Syst. , vol.54 , Issue.1 , pp. 1-9
    • Song, Q.1    Chissom, B.S.2
  • 30
    • 0000173897 scopus 로고
    • Fuzzy time series and its models
    • [30] Song, Q., Chissom, B.S., Fuzzy time series and its models. Fuzzy Sets Syst. 54:3 (1993), 269–277.
    • (1993) Fuzzy Sets Syst. , vol.54 , Issue.3 , pp. 269-277
    • Song, Q.1    Chissom, B.S.2
  • 31
    • 38149146997 scopus 로고
    • Forecasting enrollments with fuzzy time series-part II
    • [31] Song, Q., Chissom, B.S., Forecasting enrollments with fuzzy time series-part II. Fuzzy Sets Syst. 62:1 (1994), 1–8.
    • (1994) Fuzzy Sets Syst. , vol.62 , Issue.1 , pp. 1-8
    • Song, Q.1    Chissom, B.S.2
  • 32
    • 84918524947 scopus 로고    scopus 로고
    • Prediction of stock index futures prices based on fuzzy sets and multivariate fuzzy time series
    • [32] 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 151:3 (2015), 1528–1536.
    • (2015) Neurocomputing , vol.151 , Issue.3 , pp. 1528-1536
    • Sun, B.1    Guo, H.2    Karimi, H.R.3    Ge, Y.4    Xiong, S.5
  • 33
    • 77956746068 scopus 로고    scopus 로고
    • Particle swarm optimization with inertia weight variants for optimal power flow solution
    • Article ID 462145, 15 pages
    • [33] Umapathy, P., Venkataseshaiah, C., Arumugam, M.S., Particle swarm optimization with inertia weight variants for optimal power flow solution. Discrete Dyn. Nat. Soc., 2010, 2010 Article ID 462145, 15 pages (http://dx.doi.org/10.1155/2010/462145).
    • (2010) Discrete Dyn. Nat. Soc. , vol.2010
    • Umapathy, P.1    Venkataseshaiah, C.2    Arumugam, M.S.3
  • 34
    • 84922204780 scopus 로고    scopus 로고
    • Fuzzy forecasting based on automatic clustering and axiomatic fuzzy set classification
    • [34] Wang, W., Liu, X., Fuzzy forecasting based on automatic clustering and axiomatic fuzzy set classification. Inf. Sci. 294 (2015), 78–94.
    • (2015) Inf. Sci. , vol.294 , pp. 78-94
    • Wang, W.1    Liu, X.2
  • 35
    • 84974536563 scopus 로고    scopus 로고
    • A novel forecasting method based on multi-order fuzzy time series and technical analysis
    • [35] 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.
    • (2016) Inf. Sci. , vol.367-368 , pp. 41-57
    • Ye, F.1    Zhang, L.2    Zhang, D.3    Fujita, H.4    Gong, Z.5
  • 36
    • 58549116080 scopus 로고    scopus 로고
    • A new approach for determining the length of intervals for fuzzy time series
    • [36] Yolcu, U., Egrioglu, E., Uslu, V.R., Basaran, M.A., Aladag, C.H., A new approach for determining the length of intervals for fuzzy time series. Appl. Soft Comput. 9:2 (2009), 647–651.
    • (2009) Appl. Soft Comput. , vol.9 , Issue.2 , pp. 647-651
    • Yolcu, U.1    Egrioglu, E.2    Uslu, V.R.3    Basaran, M.A.4    Aladag, C.H.5
  • 37
    • 13444251121 scopus 로고    scopus 로고
    • Weighted fuzzy time-series model for TAIEX forecasting
    • [37] Yu, T.H.K., Weighted fuzzy time-series model for TAIEX forecasting. Physica A 349:3-4 (2004), 609–624.
    • (2004) Physica A , vol.349 , Issue.3-4 , pp. 609-624
    • Yu, T.H.K.1
  • 38
    • 38649123668 scopus 로고    scopus 로고
    • A bivariate fuzzy time series model to forecast the TAIEX
    • [38] Yu, T.H.K., Huarng, K.H., A bivariate fuzzy time series model to forecast the TAIEX. Expert Syst. Appl. 34:4 (2008), 2945–2952.
    • (2008) Expert Syst. Appl. , vol.34 , Issue.4 , pp. 2945-2952
    • Yu, T.H.K.1    Huarng, K.H.2
  • 39
    • 71249088973 scopus 로고    scopus 로고
    • A neural network-based fuzzy time series model to improve forecasting
    • [39] Yu, T.H.K., Huarng, K.H., A neural network-based fuzzy time series model to improve forecasting. Expert Syst. Appl. 37:4 (2010), 3366–3372.
    • (2010) Expert Syst. Appl. , vol.37 , Issue.4 , pp. 3366-3372
    • Yu, T.H.K.1    Huarng, K.H.2
  • 40
    • 77950236164 scopus 로고    scopus 로고
    • Corrigendum to “a bivariate fuzzy time series model to forecast the TAIEX
    • [Expert Systems with Applications 34 (4) (2010) 2945-2952]
    • [40] Yu, T.H.K., Huarng, K.H., Corrigendum to “a bivariate fuzzy time series model to forecast the TAIEX. [Expert Systems with Applications 34 (4) (2010) 2945-2952] Expert Syst. Appl., 37(7), 2010, 5529.
    • (2010) Expert Syst. Appl. , vol.37 , Issue.7 , pp. 5529
    • Yu, T.H.K.1    Huarng, K.H.2
  • 41
    • 34248666540 scopus 로고
    • Fuzzy sets
    • [41] Zadeh, L.A., Fuzzy sets. Inf. Control 8:3 (1965), 338–353.
    • (1965) Inf. Control , vol.8 , Issue.3 , pp. 338-353
    • Zadeh, L.A.1
  • 42
    • 85009210153 scopus 로고    scopus 로고
    • TAIEX. [Online]. Available
    • [42] TAIEX. [Online]. Available: http://www.twse.com.tw/en/products/indices/tsec/taiex.php.
  • 43
    • 85009187097 scopus 로고    scopus 로고
    • NTD/USD exchange rates. [Online]. Available
    • [43] NTD/USD exchange rates. [Online]. Available: http://www.cbc.gov.tw/content.asp?mp=1&CuItem=36599.
  • 44
    • 85009200329 scopus 로고    scopus 로고
    • Monetary Aggregate M1b. [Online]. Available
    • [44] Monetary Aggregate M1b. [Online]. Available: http://www.cbc.gov.tw/mp2.html.
  • 45
    • 85009221332 scopus 로고    scopus 로고
    • Dow Jones Industrial Average Index. [Online]. Available
    • [45] Dow Jones Industrial Average Index. [Online]. Available: http://www.djindexes.com/mdsidx/?event=historicalValuesDJI.
  • 46
    • 85009222620 scopus 로고    scopus 로고
    • NASDAQ. [Online]. Available:
    • [46] NASDAQ. [Online]. Available: http://www.nasdaq.com/symbol/nasdaq/historical.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.