Effect of Fuzzy Time Series on Smoothing Estimation of the INAR(1) Process

In this paper, the effect of fuzzy time series on estimates of the spectral, bispectral and normalized bispectral density functions are studied. This study is conducted for one of the integer autoregressive of order one (INAR(1)) models. The model of interest here is the dependent counting geometric...

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Bibliographic Details
Published inAxioms Vol. 11; no. 9; p. 423
Main Authors El-Morshedy, Mahmoud, El-Menshawy, Mohammed H., Almazah, Mohammed M. A., El-Sagheer, Rashad M., Eliwa, Mohamed S.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2022
Subjects
DCGINAR
Density
Estimates
Estimation theory
Estimators
forecasted observations
Fuzzy algorithms
Fuzzy logic
Fuzzy sets
Fuzzy systems
fuzzy time series
INAR
Mathematical models
moments
Random variables
Smoothing
Time series
Time-series analysis
Saudi Arabia
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Summary:In this paper, the effect of fuzzy time series on estimates of the spectral, bispectral and normalized bispectral density functions are studied. This study is conducted for one of the integer autoregressive of order one (INAR(1)) models. The model of interest here is the dependent counting geometric INAR(1) which is symbolized by (DCGINAR(1)). A realization is generated for this model of size n = 500 for estimation. Based on fuzzy time series, the forecasted observations of this model are obtained. The estimators of spectral, bispectral and normalized bispectral density functions are smoothed by different one- and two-dimensional lag windows. Finally, after the smoothing, all estimators are studied in the case of generated and forecasted observations of the DCGINAR(1) model. We investigate the contribution of the fuzzy time series to the smoothing of these estimates through the results.
ISSN:2075-1680
2075-1680
DOI:10.3390/axioms11090423