Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Distribution-Based Entropy Weighting Clustering of Skewed Time Series

Version 1 : Received: 30 March 2021 / Approved: 2 April 2021 / Online: 2 April 2021 (18:41:16 CEST)
Version 2 : Received: 6 April 2021 / Approved: 7 April 2021 / Online: 7 April 2021 (17:45:03 CEST)

How to cite: Mattera, R.; Giacalone, M.; Gibert Oliveiras, K. Distribution-Based Entropy Weighting Clustering of Skewed Time Series. Preprints 2021, 2021040083 (doi: 10.20944/preprints202104.0083.v1). Mattera, R.; Giacalone, M.; Gibert Oliveiras, K. Distribution-Based Entropy Weighting Clustering of Skewed Time Series. Preprints 2021, 2021040083 (doi: 10.20944/preprints202104.0083.v1).

Abstract

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 have motivated the development of classes of distributions that can accommodate properties such as heavy tails and skewness. Thanks to its flexibility, the Skew Exponential Power Distribution (also called Skew Generalized Error Distribution) ensures a unified and general framework for clustering possibly skewed time series. This paper develop 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 all the parameter estimates to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is showed by means of an application to financial time series, showing also how the obtained clusters can be used to form portfolio of stocks.

Subject Areas

Classification; Generalized Error Distribution; Skewness; Skewed Exponential Power Distribution; Portfolio selection

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