| Autor: |
Hesamian, Gholamreza, Akbari, Mohammad Ghasem |
| Předmět: |
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| Zdroj: |
IEEE Transactions on Fuzzy Systems; Oct2018, Vol. 26 Issue 5, p2953-2966, 14p |
| Abstrakt: |
This paper proposes a semiparametric autoregressive integrated moving average model for those real-world applications whose observed data are reported by fuzzy numbers. To this end, a hybrid method including nonparametric kernel-based method, least absolute deviations, and cross-validation method is suggested, which allows estimating parameters of the model including the autoregressive order $p$ , optimal value of the smoothing parameter $h$ , and fuzzy smooth function of the innovations, simultaneously. A correlation concept is also developed for fuzzy time series data and its main properties are investigated. Some common goodness-of-fit criteria are employed to examine the performance of the proposed fuzzy semiparametric time series model. A potential application of the proposed method is represented through simulated fuzzy time series data. To illustrate utility of this approach, it is applied to a set of real-life house price data in fuzzy environment. The results indicate that the proposed method is potentially effective for predicting fuzzy time series data in real applications. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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