Model-based fuzzy time series clustering of conditional higher moments

Cerqueti, Roy; Giacalone, Massimiliano; Mattera, Raffaele

doi:10.1016/j.ijar.2021.03.011

Cited by 25 publications

(20 citation statements)

References 51 publications

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“…Among the special cases, we remember the asymmetric Gauss distribution, the asymmetric uniform distribution and the asymmetric Laplace distribution. For Skewed Ged we obtained a data file consisting of 75 documents from 65 different sources (Cerqueti, 2021). Furthermore, in this research, we refer exclusively to 3 types of documents: 68 articles, 2 book chapters and 5 conference papers.…”

Section: Skewed Gedmentioning

confidence: 99%

The Impacts of Bibliometrics Measurement in the Scientific Community A Statistical Analysis of Multiple Case Studies

Basile¹,

Giacalone²,

Cozzucoli³

2022

RES

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In recent years, statistical methods such as bibliometrics have increasingly intensified to analyse books, articles, and other publications. Bibliometric methods, as techniques to measure the information distribution models, are frequently used in the field of information science and social research. The main purpose of this article is to offer scholars a general framework for the comparison between positive and negative aspects of bibliometrics, on the methods and tools used. Therefore, both the strengths and the critical points will be highlighted, to obtain a complete and detailed overview of the entire argument. In the methodological part, a bibliometric analysis will be applied to various case studies, such as with the Generalized Error Distribution, analysing and commenting on the data, and using the Bibliometrix software. The results suggest that in the future there will be greater consolidation of bibliometrics, as the introduction of increasingly advanced technologies will create new tools and methods characterized by a high degree of automation and speed.

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Section: Skewed Gedmentioning

confidence: 99%

The Impacts of Bibliometrics Measurement in the Scientific Community A Statistical Analysis of Multiple Case Studies

Basile¹,

Giacalone²,

Cozzucoli³

2022

RES

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“…Cerqueti et al. 2021 ), we consider an ACF-based distance between two pairs of time-varying parameters j and : Therefore, each matrix can be written as follows: Note that each is a squared matrix of order J and it is symmetric with a null diagonal. In the second step of the procedure we aim to cluster the N statistical units on the basis of a dissimilarity measure among the matrices .…”

Section: Multiway Clustering With Time-varying Parametersmentioning

confidence: 99%

“…Despite clustering techniques, based on time series’ distribution characteristics, have been extensively studied, approaches based on time-varying parameters have only recently been explored in Cerqueti et al. ( 2021 , 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Multiway clustering with time-varying parameters

2022

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This paper proposes a clustering approach for multivariate time series with time-varying parameters in a multiway framework. Although clustering techniques based on time series distribution characteristics have been extensively studied, methods based on time-varying parameters have only recently been explored and are missing for multivariate time series. This paper fills the gap by proposing a multiway approach for distribution-based clustering of multivariate time series. To show the validity of the proposed clustering procedure, we provide both a simulation study and an application to real air quality time series data.

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“…The spirit of most of the model-based clustering procedures is to group objects according to the estimated parameters. Important examples are the clustering methods based on ARMA process distances (for example [18,19,24]), GARCH-based distances for heteroskedastic time series [19,20,25], estimates of the probability distributions' parameters (for example [22,23]) or, more recently, conditional higher moments (for example see [26]).…”

Section: Introductionmentioning

confidence: 99%

Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series

2021

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The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.

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Model-based fuzzy time series clustering of conditional higher moments

Cited by 25 publications

References 51 publications

The Impacts of Bibliometrics Measurement in the Scientific Community A Statistical Analysis of Multiple Case Studies

The Impacts of Bibliometrics Measurement in the Scientific Community A Statistical Analysis of Multiple Case Studies

Multiway clustering with time-varying parameters

Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series

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