Kolmogorov–Zurbenko filters in spatiotemporal analysis

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