We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Nonlinear Time Series Analysis and Prediction of General Aviation Accidents Based on Multi-Timescales.
- Authors
Wang, Yufei; Zhang, Honghai; Shi, Zongbei; Zhou, Jinlun; Liu, Wenquan
- Abstract
General aviation accidents have complex interactions and influences within them that cannot be simply explained and predicted by linear models. This study is based on chaos theory and uses general aviation accident data to conduct research on different timescales (HM-scale, ET-scale, and EF-scale). First, time series are constructed by excluding seasonal patterns from the statistics of general aviation accidents. Secondly, the chaotic properties of multi-timescale series are determined by the 0–1 test and Lyapunov exponent. Finally, by introducing the sparrow search algorithm and tent chaotic mapping, a CSSA-LSSVM prediction model is proposed. The accident data of the National Transportation Safety Board (NTSB) of the United States in the past 15 years is selected for case analysis. The results show that the phase diagram of the 0–1 test presents Brownian motion characteristics, and the maximum Lyapunov exponents of the three scales are all positive, proving the chaotic characteristics of multi-timescale series. The CSSA-LSSVM prediction model's testing results illustrate its superiority in time series predicting, and when the timescale declines, the prediction error reduces gradually while the fitting effect strengthens and then decreases. This study uncovers the nonlinear chaotic features of general aviation accidents and demonstrates the significance of multi-timescale research in time series analysis and prediction.
- Subjects
UNITED States. National Transportation Safety Board; AIRCRAFT accidents; LYAPUNOV exponents; BROWNIAN motion; FORECASTING; SEARCH algorithms; CHAOS theory; TIME series analysis
- Publication
Aerospace (MDPI Publishing), 2023, Vol 10, Issue 8, p714
- ISSN
2226-4310
- Publication type
Article
- DOI
10.3390/aerospace10080714