Kolmogorov–Zurbenko filters in spatiotemporal analysis. (13th November 2017)

• Record Type: Journal Article
• Authors: Zurbenko, Igor G.; Smith, Devin
• Is Part Of: Wiley interdisciplinary reviews. Volume 10:Number 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-11-13
• Subjects: KZ Filter -- KZFT -- moving average -- spectrum estimation -- smoothing algorithm -- spatial data -- time series -- time series data; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1419
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract : This paper is an extension of WIRE publication Kolmogorov–Zurbenko filters, 2010. It addresses computational aspects of multidimensional KZ filtering for unevenly spaced data. Some real examples are provided to illustrate some of the details of such data analysis. The identification and separation of different spatial or temporal scales in spatiotemporal data can provide essential improvements to the accuracy of explanations. In particular, long term scales can be made to display clear patterns which are absolutely indistinguishable using standard multivariate analysis methods. WIREs Comput Stat 2018, 10:e1419. doi: 10.1002/wics.1419 This article is categorized under: Applications of Computational Statistics > Signal and Image Processing and Coding Data: Types and Structure > Image and Spatial Data Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Abstract : A long term KZ filter over specific humidity captures scales more than 7 years in time and more than 1000 miles in space, revealing an explosive increasing concentration of vapor energy anomalies in the North Atlantic and increasing dry out in the western Americas and Australia. These patterns essentially contribute to regional climate changes and extreme weather effects.