Effect of Fuzzy Time Series Technique on Estimators of Spectral Analysis

Teamah, Abd El-Moneim A. M.; Faied, Hasnaa M.; El-Menshawy, Mohammed H.

doi:10.9734/bpi/ramrcs/v6/1506b

Cited by 3 publications

(2 citation statements)

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“…Ref. [31] conducted research on the FTS technique to enhance periodograms while keeping their statistical characteristics. Ref.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Integer Time Series Model Estimations through Neural Network-Based Fuzzy Time Series Analysis

El-Menshawy,

Eliwa,

Al-Essa

et al. 2024

Symmetry

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This investigation explores the effects of applying fuzzy time series (FTSs) based on neural network models for estimating a variety of spectral functions in integer time series models. The focus is particularly on the skew integer autoregressive of order one (NSINAR(1)) model. To support this estimation, a dataset consisting of NSINAR(1) realizations with a sample size of n = 1000 is created. These input values are then subjected to fuzzification via fuzzy logic. The prowess of artificial neural networks in pinpointing fuzzy relationships is harnessed to improve prediction accuracy by generating output values. The study meticulously analyzes the enhancement in smoothing of spectral function estimators for NSINAR(1) by utilizing both input and output values. The effectiveness of the output value estimates is evaluated by comparing them to input value estimates using a mean-squared error (MSE) analysis, which shows how much better the output value estimates perform.

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“…Ref. [31] conducted research on the FTS technique to enhance periodograms while keeping their statistical characteristics. Ref.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Integer Time Series Model Estimations through Neural Network-Based Fuzzy Time Series Analysis

El-Menshawy,

Eliwa,

Al-Essa

et al. 2024

Symmetry

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“…Ref. [30] studied the effect of fuzzy time series technique on estimates of spectral analysis. Our goal in this paper is to see the effect of fuzzy time series on improving the smoothing of integer-valued time series estimates.…”

Section: Introductionmentioning

confidence: 99%

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

El-Morshedy

El-Menshawy

Almazah

et al. 2022

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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.

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A New Statistical Technique to Enhance MCGINAR(1) Process Estimates under Symmetric and Asymmetric Data: Fuzzy Time Series Markov Chain and Its Characteristics

et al. 2023

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Several models for time series with integer values have been published as a result of the substantial demand for the description of process stability having discrete marginal distributions. One of these models is the mixed count geometric integer autoregressive of order one (MCGINAR(1)), which is based on two thinning operators. This study examines how the estimates of the spectral density functions of the MCGINAR(1) model are affected by fuzzy time series Markov chain (FTSMC). Regarding this study’s context, the higher-order moments, central moments and spectral density functions of MCGINAR(1) are computed. The anticipated realizations of the generated realizations for this model are obtained based on FTSMC. In the case of generated and anticipated realizations, several lag windows are used to smooth the spectral density estimators. The generated realization estimates are compared with the anticipated realization estimates using the MSE to ascertain the FTSMC’s role in improving the estimation process.

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Effect of Fuzzy Time Series Technique on Estimators of Spectral Analysis

Cited by 3 publications

References 0 publications

Enhancing Integer Time Series Model Estimations through Neural Network-Based Fuzzy Time Series Analysis

Enhancing Integer Time Series Model Estimations through Neural Network-Based Fuzzy Time Series Analysis

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

A New Statistical Technique to Enhance MCGINAR(1) Process Estimates under Symmetric and Asymmetric Data: Fuzzy Time Series Markov Chain and Its Characteristics

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