Document Type
Article
Publication Date
10-2023
Abstract
Purpose: Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.
Design/Methodology/Approach: Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lighting prediction.
Findings: The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.
Abstract © Emerald Publishing Limited
Source Publication
Journal of Defense Analytics and Logistics
Recommended Citation
Nystrom, J., Hill, R. R., Geyer, A., Pignatiello, J. J., & Chicken, E. (2023). Lightning forecast from chaotic and incomplete time series using wavelet de-noising and spatiotemporal kriging. Journal of Defense Analytics and Logistics, 7(2), 90–102. https://doi.org/10.1108/JDAL-03-2023-0003
Comments
All articles published in JDAL are published Open Access under a Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. CC BY 4.0
Sourced from the publisher version of record at Emerald, as cited below and DOI-linked.