| Autor: |
Gupta, Aditi, Gupta, Sukanya, Onumanyi, Adeiza J., Ahlawat, Satyadev, Prasad, Yamuna, Singh, Virendra |
| Předmět: |
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| Zdroj: |
Cluster Computing; Jul2024, Vol. 27 Issue 4, p4063-4076, 14p |
| Abstrakt: |
The identification of peaks in time series data, known as peak detection (PD), holds great significance as it pinpoints notable fluctuations within the dataset. These peaks serve as crucial indicators of transitions or anomalies in the time series. This technique finds utility across various applications including spectroscopy, biomedical image processing, and noise signal differentiation, among other domains. The challenge lies in autonomously estimating parameters for a generalized online peak detection algorithm due to the dynamic nature of real-time time series data. Moreover, automating parameter estimation is a major obstacle, given that different domains require adjustments on varying scales. Addressing this challenge, we propose A-TSPD (autonomous-two stage peak detection), a novel algorithm building upon the TSPD (two stage peak detection) algorithm. A-TSPD aims to achieve autonomous parameter estimation, overcoming the need for manual configuration. Experimental validation on seven real-world datasets across diverse domains demonstrates the efficacy of A-TSPD, showcasing significant performance improvements compared to other established techniques. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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