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Fuzzy Time Series Modeling Approaches: A Review

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Applications of Soft Computing in Time Series Forecasting

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 330))

Abstract

Recently, there seems to be increased interest in time series forecasting using soft computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set (RS) and evolutionary computing (EC). Among them, fuzzy set is widely used technique in this domain, which is referred to as “Fuzzy Time Series (FTS)”. In this chapter, extensive information and knowledge are provided for the FTS concepts and their applications in time series forecasting. This chapter reviews and summarizes previous research works in the FTS modeling approach from the period 1993–2013 (June). Here, we also provide a brief introduction to SC techniques, because in many cases problems can be solved most effectively by integrating these techniques into different phases of the FTS modeling approach. Hence, several techniques that are hybridized with the FTS modeling approach are discussed briefly. We also identified various domains specific problems and research trends, and try to categorize them. The chapter ends with the implication for future works. This review may serve as a stepping stone for the amateurs and advanced researchers in this domain.

Although this may seem a paradox, all exact science is dominated by the concept of approximation. By Bertrand Shaw (1872–1970)

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Notes

  1. 1.

    References are: (Zadeh 1971, 1973, 1975a).

  2. 2.

    References are: (Song and Chissom 1993a, b, 1994).

  3. 3.

    References are: (Chen 1996; Song and Chissom 1993a, 1994).

  4. 4.

    References are: (Chen 1996; Song and Chissom 1993a, 1994).

  5. 5.

    References are: (Chang et al. 2007; Chen 1996; Cheng et al. 2006; Huarng 2001; Hwang et al. 1998; Song and Chissom 1993a, b, 1994).

  6. 6.

    References are: (Aladag et al. 2009, 2010; Avazbeigi et al. 2010; Bahrepour et al. 2011; Chen 2002; Chen and Chen 2011a, b; Chen and Chung 2006b; Chen et al 2008; Gangwar and Kumar 2012; Jilani and Burney 2008; Own and Yu 2005; Singh 2007a, c, 2008, 2009; Tsai and Wu 2000).

  7. 7.

    References are: (Chen 1996, 2002; Cheng et al. 2008b; Huarng 2001; Huarng et al 2007; Hwang et al. 1998; Jilani and Burney 2008; Kuo et al. 2009; Lee et al. 2006; Li et al. 2008; Qiu et al 2011; Singh and Borah 2012, 2013b; Singh 2007a, b, 2009; Yu 2005b).

  8. 8.

    References are: (Huang et al. 2011a, b; Kuo et al. 2009, 2010).

  9. 9.

    References are: (Huang et al. 2011a, b; Kuo et al. 2009, 2010).

  10. 10.

    References are: (Cheng et al. 2013; Huarng and Yu 2012; Wei et al 2011; Yu and Huarng 2010).

  11. 11.

    References are: (Aladag et al. 2012; Avazbeigi et al. 2010; Bai et al. 2011; Kuo et al. 2010).

  12. 12.

    References are: (Egrioglu et al. 2010, 2011a; Sah and Degtiarev 2005; Wong et al. 2010; Yu 2005a).

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  1. Department of Computer Science and Engineering, Thapar University, Patiala, India

    Pritpal Singh

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Singh, P. (2016). Fuzzy Time Series Modeling Approaches: A Review. In: Applications of Soft Computing in Time Series Forecasting. Studies in Fuzziness and Soft Computing, vol 330. Springer, Cham. https://doi.org/10.1007/978-3-319-26293-2_2

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