Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization
Time-series generated by complex systems (CS) are often characterized by phenomena such as chaoticity, fractality and memory effects, which pose difficulties in their analysis. The paper explores the dynamics of multidimensional data generated by a CS. The Dow Jones Industrial Average (DJIA) index i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/23/5/600 |
| _version_ | 1827692642086944768 |
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| author | António M. Lopes Jóse A. Tenreiro Machado |
| author_facet | António M. Lopes Jóse A. Tenreiro Machado |
| author_sort | António M. Lopes |
| collection | DOAJ |
| description | Time-series generated by complex systems (CS) are often characterized by phenomena such as chaoticity, fractality and memory effects, which pose difficulties in their analysis. The paper explores the dynamics of multidimensional data generated by a CS. The Dow Jones Industrial Average (DJIA) index is selected as a test-bed. The DJIA time-series is normalized and segmented into several time window vectors. These vectors are treated as objects that characterize the DJIA dynamical behavior. The objects are then compared by means of different distances to generate proper inputs to dimensionality reduction and information visualization algorithms. These computational techniques produce meaningful representations of the original dataset according to the (dis)similarities between the objects. The time is displayed as a parametric variable and the non-locality can be visualized by the corresponding evolution of points and the formation of clusters. The generated portraits reveal a complex nature, which is further analyzed in terms of the emerging patterns. The results show that the adoption of dimensionality reduction and visualization tools for processing complex data is a key modeling option with the current computational resources. |
| first_indexed | 2024-03-10T11:26:55Z |
| format | Article |
| id | doaj.art-7a5c5019768d482eafdf723d688db817 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T11:26:55Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-7a5c5019768d482eafdf723d688db8172023-11-21T19:32:53ZengMDPI AGEntropy1099-43002021-05-0123560010.3390/e23050600Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and VisualizationAntónio M. Lopes0Jóse A. Tenreiro Machado1LAETA/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, PortugalDepartment of Electrical Engineering, Institute of Engineering, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, PortugalTime-series generated by complex systems (CS) are often characterized by phenomena such as chaoticity, fractality and memory effects, which pose difficulties in their analysis. The paper explores the dynamics of multidimensional data generated by a CS. The Dow Jones Industrial Average (DJIA) index is selected as a test-bed. The DJIA time-series is normalized and segmented into several time window vectors. These vectors are treated as objects that characterize the DJIA dynamical behavior. The objects are then compared by means of different distances to generate proper inputs to dimensionality reduction and information visualization algorithms. These computational techniques produce meaningful representations of the original dataset according to the (dis)similarities between the objects. The time is displayed as a parametric variable and the non-locality can be visualized by the corresponding evolution of points and the formation of clusters. The generated portraits reveal a complex nature, which is further analyzed in terms of the emerging patterns. The results show that the adoption of dimensionality reduction and visualization tools for processing complex data is a key modeling option with the current computational resources.https://www.mdpi.com/1099-4300/23/5/600dimensionality reductiondata visualizationclusteringtime-seriescomplex systems |
| spellingShingle | António M. Lopes Jóse A. Tenreiro Machado Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization Entropy dimensionality reduction data visualization clustering time-series complex systems |
| title | Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization |
| title_full | Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization |
| title_fullStr | Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization |
| title_full_unstemmed | Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization |
| title_short | Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization |
| title_sort | dynamical analysis of the dow jones index using dimensionality reduction and visualization |
| topic | dimensionality reduction data visualization clustering time-series complex systems |
| url | https://www.mdpi.com/1099-4300/23/5/600 |
| work_keys_str_mv | AT antoniomlopes dynamicalanalysisofthedowjonesindexusingdimensionalityreductionandvisualization AT joseatenreiromachado dynamicalanalysisofthedowjonesindexusingdimensionalityreductionandvisualization |