Autoregressive and moving average models for zero‐inflated count time series

Sathish, Vurukonda; Mukhopadhyay, Susanta; Tiwari, Ravi

doi:10.1111/stan.12255

Cited by 6 publications

(3 citation statements)

References 59 publications

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“…These contributions allow the analysis of time series, such as the number of disease cases in populations, monitoring and predicting periods and probabilities of outbreaks over time, as discussed in Yang, Zamba and Cavanaugh (2013) and Sathish, Mukhopadhyay and Tiwari (2021).…”

Section: Discussionmentioning

confidence: 99%

Count time series with excess zeros: A Bayesian perspective using zero-adjusted distributions

Pala

Carvalho

Sáfadi

2022

Semina: Ciênc. Ex. Tech.

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Models for count data which are temporally correlated have been studied using many conditional distributions, such as the Poisson distribution, and the insertion of different dependence structures. Nonetheless, excess of zeros and over dispersion may be observed during the counting process and need to be considered when modelling and choosing a conditional distribution. In this paper, we propose models for counting time series using zero-adjusted distributions by inserting a dependence structure following the ARMA(p, q) process on a Bayesian framework. We perform a simulation study using the proposed Bayesian analysis and analyse the monthly time series of the number of deaths due to dengue haemorrhagic fever (ICD-A91) in Brazil.

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Section: Discussionmentioning

confidence: 99%

Count time series with excess zeros: A Bayesian perspective using zero-adjusted distributions

Pala

Carvalho

Sáfadi

2022

Semina: Ciênc. Ex. Tech.

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“…Researchers have proposed different forms of targeted neural networks to utilize these different forms of data better. For example, the more representative ones are convolutional neural networks for image data tasks and LSTM-based recurrent neural networks for time-series data tasks [16].…”

Section: Deep Neural Networkmentioning

confidence: 99%

Exploring the teaching practice of visual communication design in colleges and universities under the background of big data

Yuan,

Wang

2023

Applied Mathematics and Nonlinear Sciences

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Under the digitalization of big data information, how to use big data technology to combine with visual communication design teaching has become a key topic of concern in the current education sector. Firstly, this paper proposes using a deep learning model based on big data technology for teaching practice research of visual communication design majors in colleges and universities and constructs a deep learning model by combining perceptron and bp neural algorithm for data mining property and accurate prediction. Secondly, the deep learning model is optimized by using the activation function and knowledge distillation function in response to the limitation problem of the deep learning model. Then the teaching evaluation data of visual communication design majors in colleges and universities are obtained through questionnaire survey work, and evaluation indexes are determined according to the evaluation data. Finally, students of visual design majors at Guizhou Vocational and Technical College were selected as research samples, and the practice performance of communication design majors was analyzed based on the deep learning model. The results showed that the deep learning model: from week 1 to week 20, the score improved from 52 to 88, which improved the score by 69.23%. The traditional method: from week 1 to week 20, the score improved from 52 to 80, improving the score by 53.84%. This study is conducive to promoting the healthy development of the visual communication profession and cultivating design talents that meet the needs of the information age and is of great significance to the development of the visual communication profession in China.

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“…High proportions of zeros cannot be disregarded during modeling, as they can affect the inference and lead to spurious relationships (Alqawba et al, 2019). Considering this aspect, researchers have focused on exploring zeroinflated distributions such as zero-inflated Poisson (ZIP) and zero-inflated negative Binomial (ZINB), as well as their zero-adjusted versions such as zero-adjusted Poisson (ZAP) (Alqawba et al, 2019;Ghahramani & White, 2020;Sales et al, 2022;Sathish et al, 2021;Tawiah et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Zero-Adjusted Poisson Distribution for Modeling Count Time Series

Pala,

Sáfadi

2024

Semin., Exact Tech. Scienc.

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Many studies have used extensions of ARMA models for the analysis of non-Gaussian time series. One of them is the Generalized Autoregressive Moving Average, GARMA, enabling the modeling of count time series with distributions such as Poisson. The GARMA class is being expanded to accommodate other distributions, aiming to capture the typical characteristics of count data, including under or overdispersion and excess zeros. This study aims to propose an approach based on the GARMA class in order to analyze count time series with excess zeros, assuming a time-varying zero-adjusted Poisson distribution. This approach allows for capturing serial correlation, forecasting the future values, and estimating the future probability of zeros. For inference, a Bayesian analysis was adopted using the Hamiltonian Monte Carlo (HMC) algorithm for sampling from the joint posterior distribution. We conducted a simulation study and presented an application to influenza mortality reported in Brazil. Our findings demonstrated the usefulness of the model in estimating the probability of non-occurrence and the number of counts in future periods.

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Autoregressive and moving average models for zero‐inflated count time series

Cited by 6 publications

References 59 publications

Count time series with excess zeros: A Bayesian perspective using zero-adjusted distributions

Count time series with excess zeros: A Bayesian perspective using zero-adjusted distributions

Exploring the teaching practice of visual communication design in colleges and universities under the background of big data

Time-Varying Zero-Adjusted Poisson Distribution for Modeling Count Time Series

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