Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/13/6/959 |
| _version_ | 1797532304077225984 |
|---|---|
| author | Raffaele Mattera Massimiliano Giacalone Karina Gibert |
| author_facet | Raffaele Mattera Massimiliano Giacalone Karina Gibert |
| author_sort | Raffaele Mattera |
| collection | DOAJ |
| description | 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 <i>k</i>-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. |
| first_indexed | 2024-03-10T10:57:13Z |
| format | Article |
| id | doaj.art-164ce3554f7e4a258515074823c826c8 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T10:57:13Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-164ce3554f7e4a258515074823c826c82023-11-21T21:45:10ZengMDPI AGSymmetry2073-89942021-05-0113695910.3390/sym13060959Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time SeriesRaffaele Mattera0Massimiliano Giacalone1Karina Gibert2Department of Economics and Statistics, University of Naples “Federico II”, 80126 Naples, ItalyDepartment of Economics and Statistics, University of Naples “Federico II”, 80126 Naples, ItalyIntelligent Data Science and Artificial Intelligence Research Center, Universitat Politecnica de Catalunya, 08034 Barcelona, SpainThe 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 <i>k</i>-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.https://www.mdpi.com/2073-8994/13/6/959classificationgeneralized error distributionskewnessskewed exponential power distributionfinancial time seriesportfolio selection |
| spellingShingle | Raffaele Mattera Massimiliano Giacalone Karina Gibert Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series Symmetry classification generalized error distribution skewness skewed exponential power distribution financial time series portfolio selection |
| title | Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series |
| title_full | Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series |
| title_fullStr | Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series |
| title_full_unstemmed | Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series |
| title_short | Distribution-Based Entropy Weighting Clustering of Skewed and Heavy Tailed Time Series |
| title_sort | distribution based entropy weighting clustering of skewed and heavy tailed time series |
| topic | classification generalized error distribution skewness skewed exponential power distribution financial time series portfolio selection |
| url | https://www.mdpi.com/2073-8994/13/6/959 |
| work_keys_str_mv | AT raffaelemattera distributionbasedentropyweightingclusteringofskewedandheavytailedtimeseries AT massimilianogiacalone distributionbasedentropyweightingclusteringofskewedandheavytailedtimeseries AT karinagibert distributionbasedentropyweightingclusteringofskewedandheavytailedtimeseries |