A new approach for determining the length of intervals for fuzzy time series

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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A new approach for determining the length of intervals for fuzzy time series. / Yolcu, Ufuk; Egrioglu, Erol; Uslu, Vedide R. et al.
In: Applied Soft Computing Journal, Vol. 9, No. 2, 01.03.2009, p. 647-651.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Yolcu, U, Egrioglu, E, Uslu, VR, Basaran, MA & Aladag, CH 2009, 'A new approach for determining the length of intervals for fuzzy time series', Applied Soft Computing Journal, vol. 9, no. 2, pp. 647-651. https://doi.org/10.1016/j.asoc.2008.09.002

APA

Yolcu, U., Egrioglu, E., Uslu, V. R., Basaran, M. A., & Aladag, C. H. (2009). A new approach for determining the length of intervals for fuzzy time series. Applied Soft Computing Journal, 9(2), 647-651. https://doi.org/10.1016/j.asoc.2008.09.002

Vancouver

Yolcu U, Egrioglu E, Uslu VR, Basaran MA, Aladag CH. A new approach for determining the length of intervals for fuzzy time series. Applied Soft Computing Journal. 2009 Mar 1;9(2):647-651. doi: 10.1016/j.asoc.2008.09.002

Author

Yolcu, Ufuk ; Egrioglu, Erol ; Uslu, Vedide R. et al. / A new approach for determining the length of intervals for fuzzy time series. In: Applied Soft Computing Journal. 2009 ; Vol. 9, No. 2. pp. 647-651.

Bibtex

@article{1b65662612584ed0a5d5c74c85f3517e,

title = "A new approach for determining the length of intervals for fuzzy time series",

abstract = "In the implementations of fuzzy time series forecasting, the identification of interval lengths has an important impact on the performance of the procedure. However, the interval length has been chosen arbitrarily in many papers. Huarng developed a new approach which is called ratio-based lengths of intervals in order to identify the length of intervals. In our paper, we propose a new approach which uses a single-variable constrained optimization to determine the ratio for the length of intervals. The proposed approach is applied to the two well-known time series, which are enrollment data at The University of Alabama and inventory demand data. The obtained results are compared to those of other methods. The proposed method produces more accurate predictions for the future values of used time series.",

keywords = "Forecasting, Fuzzy sets, Fuzzy time series, Length of interval, Optimization",

author = "Ufuk Yolcu and Erol Egrioglu and Uslu, {Vedide R.} and Basaran, {Murat A.} and Aladag, {Cagdas H.}",

year = "2009",

month = mar,

day = "1",

doi = "10.1016/j.asoc.2008.09.002",

language = "English",

volume = "9",

pages = "647--651",

journal = "Applied Soft Computing Journal",

issn = "1568-4946",

publisher = "Elsevier Science B.V.",

number = "2",

}

RIS

TY - JOUR

T1 - A new approach for determining the length of intervals for fuzzy time series

AU - Yolcu, Ufuk

AU - Egrioglu, Erol

AU - Uslu, Vedide R.

AU - Basaran, Murat A.

AU - Aladag, Cagdas H.

PY - 2009/3/1

Y1 - 2009/3/1

N2 - In the implementations of fuzzy time series forecasting, the identification of interval lengths has an important impact on the performance of the procedure. However, the interval length has been chosen arbitrarily in many papers. Huarng developed a new approach which is called ratio-based lengths of intervals in order to identify the length of intervals. In our paper, we propose a new approach which uses a single-variable constrained optimization to determine the ratio for the length of intervals. The proposed approach is applied to the two well-known time series, which are enrollment data at The University of Alabama and inventory demand data. The obtained results are compared to those of other methods. The proposed method produces more accurate predictions for the future values of used time series.

AB - In the implementations of fuzzy time series forecasting, the identification of interval lengths has an important impact on the performance of the procedure. However, the interval length has been chosen arbitrarily in many papers. Huarng developed a new approach which is called ratio-based lengths of intervals in order to identify the length of intervals. In our paper, we propose a new approach which uses a single-variable constrained optimization to determine the ratio for the length of intervals. The proposed approach is applied to the two well-known time series, which are enrollment data at The University of Alabama and inventory demand data. The obtained results are compared to those of other methods. The proposed method produces more accurate predictions for the future values of used time series.

KW - Forecasting

KW - Fuzzy sets

KW - Fuzzy time series

KW - Length of interval

KW - Optimization

U2 - 10.1016/j.asoc.2008.09.002

DO - 10.1016/j.asoc.2008.09.002

M3 - Journal article

AN - SCOPUS:58549116080

VL - 9

SP - 647

EP - 651

JO - Applied Soft Computing Journal

JF - Applied Soft Computing Journal

SN - 1568-4946

IS - 2

ER -

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