Autoregressive Fractionally Integrated Moving Average-Generalized Autoregressive Conditional Heteroskedasticity Model with Level Shift Intervention

Dhliwayo, Lawrence; Matarise, Florance; Chimedza, Charles

doi:10.4236/ojs.2020.102023

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…The ARFIMA model can be fitted by minimizing the Akaike Information Criterion (AIC). For more details, see [26].…”

Section: Arfimamentioning

confidence: 99%

Combining Multiple Testing with Multivariate Singular Spectrum Analysis

Movahedifar¹,

Dickhaus²

2022

Preprint

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Appropriate preprocessing is a fundamental prerequisite for analyzing a noisy dataset. The purpose of this paper is to apply a nonparametric preprocessing method, called Singular Spectrum Analysis (SSA), to a variety of datasets which are subsequently analyzed by means of multiple statistical hypothesis tests. SSA is a nonparametric preprocessing method which has recently been utilized in the context of many life science problems. In the present work, SSA is compared with three other state-of-the-art preprocessing methods in terms of goodness of denoising and in terms of the statistical power of the subsequent multiple test. These other methods are either parametric or nonparametric. Our findings demonstrate that (multivariate) SSA can be taken into account as a promising method to reduce noise, to extract the main signal from noisy data, and to detect statistically significant signal components.

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“…The ARFIMA model can be fitted by minimizing the Akaike Information Criterion (AIC). For more details, see [26].…”

Section: Arfimamentioning

confidence: 99%

Combining Multiple Testing with Multivariate Singular Spectrum Analysis

Movahedifar¹,

Dickhaus²

2022

Preprint

View full text Add to dashboard Cite

show abstract

A multimodal hybrid stochastic-based deterministic ARFIMA model for the sustainable analysis of COVID-19 pandemic

Bukhari

Ahmed

Raja

et al. 2023

Waves in Random and Complex Media

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Modeling Seasonal Fractionally Integrated Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity Model with Seasonal Level Shift Intervention

Dhliwayo¹,

Matarise²,

Chimedza³

2020

OJS

Self Cite

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This paper introduces the class of seasonal fractionally integrated autoregressive moving average-generalized conditional heteroskedastisticty (SARFIMA-GARCH) models, with level shift type intervention that are capable of capturing simultaneously four key features of time series: seasonality, long range dependence, volatility and level shift. The main focus is on modeling seasonal level shift (SLS) in fractionally integrated and volatile processes. A natural extension of the seasonal level shift detection test of the mean for a realization of time series satisfying SLS-SARFIMA and SLS-GARCH models was derived. Test statistics that are useful to examine if seasonal level shift in an SARFIMA-GARCH model is statistically plausible were established. Estimation of SLS-SARFIMA and SLS-GARCH parameters was also considered.

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Autoregressive Fractionally Integrated Moving Average-Generalized Autoregressive Conditional Heteroskedasticity Model with Level Shift Intervention

Cited by 3 publications

References 30 publications

Combining Multiple Testing with Multivariate Singular Spectrum Analysis

Combining Multiple Testing with Multivariate Singular Spectrum Analysis

A multimodal hybrid stochastic-based deterministic ARFIMA model for the sustainable analysis of COVID-19 pandemic

Modeling Seasonal Fractionally Integrated Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroscedasticity Model with Seasonal Level Shift Intervention

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