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**Effective lengths of intervals to improve forecasting in fuzzy time series.**
*(English)*
Zbl 0992.91077

Summary: Length of intervals affects forecasting results in fuzzy time series. Unfortunately, the issue of how to determine effective lengths of intervals has not been touched in previous studies. This study proposes distribution- and average-based length to approach this issue. Distribution-based length is the largest length smaller than at least half the first differences of data. Average-based length is set to one half the average of the first differences of data. Empirical analyses show that distribution- and average-based lengths are simple to calculate and can greatly improve forecasting results; in particular, they are superior to the randomly chosen lengths used in previous studies.

### MSC:

91B84 | Economic time series analysis |

62A86 | Fuzzy analysis in statistics |

62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |

03E72 | Theory of fuzzy sets, etc. |

Full Text:
DOI

### References:

[1] | Chen, S.-M., Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems, 81, 311-319 (1996) |

[2] | Song, Q.; Chissom, B. S., Fuzzy time series and its models, Fuzzy Sets and Systems, 54, 269-277 (1993) · Zbl 0792.62087 |

[3] | Song, Q.; Chissom, B. S., Forecasting enrollments with fuzzy time series – part 1, Fuzzy Sets and Systems, 54, 1-9 (1993) |

[4] | Song, Q.; Chissom, B. S., Forecasting enrollments with fuzzy time series – part 2, Fuzzy Sets and Systems, 62, 1-8 (1994) |

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