A Systematic Review of INGARCH Models for Integer-Valued Time Series

Liu, Mengya; Zhu, Fukang; Li, Jianfeng; Sun, Changqing

doi:10.3390/e25060922

Cited by 6 publications

(2 citation statements)

References 105 publications

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“…Here, it is possible to use both purely statistical methods [63,64] and machine learning methods. Also, of undoubted interest, are the recent works by [68] on practical techniques with ordinal series and the works of [76,77] with count time series.…”

Section: Main Propertiesmentioning

confidence: 99%

Modeling Seasonality of Emotional Tension in Social Media

Nosov,

Kuznetsova,

Stankevich

et al. 2023

Computers

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Social media has become an almost unlimited resource for studying social processes. Seasonality is a phenomenon that significantly affects many physical and mental states. Modeling collective emotional seasonal changes is a challenging task for the technical, social, and humanities sciences. This is due to the laboriousness and complexity of obtaining a sufficient amount of data, processing and evaluating them, and presenting the results. At the same time, understanding the annual dynamics of collective sentiment provides us with important insights into collective behavior, especially in various crises or disasters. In our study, we propose a scheme for identifying and evaluating signs of the seasonal rise and fall of emotional tension based on social media texts. The analysis is based on Russian-language comments in VKontakte social network communities devoted to city news and the events of a small town in the Nizhny Novgorod region, Russia. Workflow steps include a statistical method for categorizing data, exploratory analysis to identify common patterns, data aggregation for modeling seasonal changes, the identification of typical data properties through clustering, and the formulation and validation of seasonality criteria. As a result of seasonality modeling, it is shown that the calendar seasonal model corresponds to the data, and the dynamics of emotional tension correlate with the seasons. The proposed methodology is useful for a wide range of social practice issues, such as monitoring public opinion or assessing irregular shifts in mass emotions.

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Section: Main Propertiesmentioning

confidence: 99%

Modeling Seasonality of Emotional Tension in Social Media

Nosov,

Kuznetsova,

Stankevich

et al. 2023

Computers

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show abstract

“…Additionally, Weiß and Jahn [ 17 ] introduced soft-clipping binomial INGARCH models as time series models for bounded counts, which also accommodate negative autocorrelations. Liu et al [ 18 ] presented a review of the developments in INGARCH models over the past five years, focusing on unbounded and bounded non-negative counts.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Forecasting of Bounded Poisson Distributed Time Series

Liu,

Chen,

2023

Entropy

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This research models and forecasts bounded ordinal time series data that can appear in various contexts, such as air quality index (AQI) levels, economic situations, and credit ratings. This class of time series data is characterized by being bounded and exhibiting a concentration of large probabilities on a few categories, such as states 0 and 1. We propose using Bayesian methods for modeling and forecasting in zero-one-inflated bounded Poisson autoregressive (ZOBPAR) models, which are specifically designed to capture the dynamic changes in such ordinal time series data. We innovatively extend models to incorporate exogenous variables, marking a new direction in Bayesian inferences and forecasting. Simulation studies demonstrate that the proposed methods accurately estimate all unknown parameters, and the posterior means of parameter estimates are robustly close to the actual values as the sample size increases. In the empirical study we investigate three datasets of daily AQI levels from three stations in Taiwan and consider five competing models for the real examples. The results exhibit that the proposed method reasonably predicts the AQI levels in the testing period, especially for the Miaoli station.

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Softplus negative binomial network autoregression

Guo,

Zhu

2024

Stat

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Modelling multivariate time series of counts in a parsimonious way is a popular topic. In this paper, we consider an integer‐valued network autoregressive model with a non‐random neighbourhood structure, which uses negative binomial distribution as the conditional marginal distribution and the softplus function as the link function. The new model generalizes existing ones in the literature and has a great flexibility in modelling. Stationary conditions in cases of fixed dimension and increasing dimension are given. Parameters are estimated by maximizing the quasi‐likelihood function, and related asymptotic properties of the estimators are established. A simulation study is conducted to assess performances of the estimators, and a real data example is analysed to show superior performances of the proposed model compared with existing ones.

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A Systematic Review of INGARCH Models for Integer-Valued Time Series

Cited by 6 publications

References 105 publications

Modeling Seasonality of Emotional Tension in Social Media

Modeling Seasonality of Emotional Tension in Social Media

Bayesian Forecasting of Bounded Poisson Distributed Time Series

Softplus negative binomial network autoregression

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