Shannon entropy-based complexity quantification of nonlinear stochastic process

Karaca, Yeliz; Moonis, Majaz

doi:10.1016/b978-0-323-90032-4.00018-3

Cited by 4 publications

(1 citation statement)

References 43 publications

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“…where P is the probability of occurrence, and S is the data values [33][34][35]. Sample entropy (SplE) is used to extract information about the complexity of the time-series data by quantifying the regularity and unpredictability of its fluctuations is given as…”

Section: Methodsmentioning

confidence: 99%

Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods

Alves,

Mendonça,

Mostafa

et al. 2023

Applied Sciences

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Wind factors significantly influence air travel, and extreme conditions can cause operational disruptions. Machine learning approaches are emerging as a valuable tool for predicting wind patterns. This research, using Madeira International Airport as a case study, delves into the effectiveness of feature creation and selection for wind nowcasting, focusing on predicting wind speed, direction, and gusts. Data from four sensors provided 56 features to forecast wind conditions over intervals of 2, 10, and 20 min. Five feature selection techniques were analyzed, namely mRMR, PCA, RFECV, GA, and XGBoost. The results indicate that combining new wind features with optimized feature selection can boost prediction accuracy and computational efficiency. A strong spatial correlation was observed among sensors at different locations, suggesting that the spatial-temporal context enhances predictions. The best accuracy for wind speed forecasts yielded a mean absolute percentage error of 0.35%, 0.53%, and 0.63% for the three time intervals, respectively. Wind gust errors were 0.24%, 0.33%, and 0.38%, respectively, while wind direction predictions remained challenging with errors above 100% for all intervals.

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Section: Methodsmentioning

confidence: 99%