A new approach for optimal time-series segmentation

Áreas de investigación:

• Methodology - Time series

Año:

2020

Tipo de publicación:

Artículo

Autores:

• Carmona-Poyato, Ángel
• Fernández-García, Nicolás Luis
• Madrid-Cuevas, Francisco José
• Durán-Rosal, Antonio Manuel

Journal:

Pattern Recognition Letters

Volumen:

135

Páginas:

153-159

Mes:

July

ISSN:

0167-8655

BibTex:

@article{Duran2019_optimal, author = "{\'A}ngel Carmona-Poyato and Nicol{\'a}s Luis Fern{\'a}ndez-Garc{\'i}a and Francisco Jos{\'e} Madrid-Cuevas and Antonio Manuel Dur{\'a}n-Rosal", abstract = "Emerging technologies have led to the creation of huge databases that require reducing their high dimensionality to be analysed. Many suboptimal methods have been proposed for this purpose. On the other hand, few efficient optimal methods have been proposed due to their high computational complexity. However, these methods are necessary to evaluate the performance of suboptimal methods. This paper proposes a new optimal approach, called OSTS, to improve the segmentation of time series. The proposed method is based on A* algorithm and it uses an improved version of the well-known Salotti method for obtaining optimal polygonal approximations. Firstly, a suboptimal method for time-series segmentation is applied to obtain pruning values. In this case, a suboptimal method based on Bottom-Up technique is selected. Then, the results of the suboptimal method are used as pruning values to reduce the computational time of the proposed method. The proposal has been compared to other suboptimal methods and the results have shown that the method is optimal, and, in some cases, the computational time is similar to other suboptimal methods.", awards = "JCR(2020): 3.756 Position: 46/140 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2020): 3.756 Position: 46/140 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.patrec.2020.04.006", issn = "0167-8655", journal = "Pattern Recognition Letters", month = "July", note = "JCR(2020): 3.756 Position: 46/140 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "153-159", title = "{A} new approach for optimal time-series segmentation", url = "doi.org/10.1016/j.patrec.2020.04.006", volume = "135", year = "2020", }

Nota:

JCR(2020): 3.756 Position: 46/140 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

Emerging technologies have led to the creation of huge databases that require reducing their high dimensionality to be analysed. Many suboptimal methods have been proposed for this purpose. On the other hand, few efficient optimal methods have been proposed due to their high computational complexity. However, these methods are necessary to evaluate the performance of suboptimal methods. This paper proposes a new optimal approach, called OSTS, to improve the segmentation of time series. The proposed method is based on A* algorithm and it uses an improved version of the well-known Salotti method for obtaining optimal polygonal approximations. Firstly, a suboptimal method for time-series segmentation is applied to obtain pruning values. In this case, a suboptimal method based on Bottom-Up technique is selected. Then, the results of the suboptimal method are used as pruning values to reduce the computational time of the proposed method. The proposal has been compared to other suboptimal methods and the results have shown that the method is optimal, and, in some cases, the computational time is similar to other suboptimal methods.

Comentarios:

JCR(2020): 3.756 Position: 46/140 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE