A new approach for optimal time-series segmentation

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Research areas:
Year:
2020
Type of Publication:
Article
Authors:
Journal:
Pattern Recognition Letters
Volume:
135
Pages:
153-159
Month:
July
ISSN:
0167-8655
Note:
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.
Comments:
JCR(2020): 3.756 Position: 46/140 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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