Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

8-2015

Journal or Book Title

Environmetrics

Volume

26

Issue

5

First Page

354

Last Page

362

DOI

10.1002/env.2344

Abstract

A new methodology is proposed for the analysis, modeling, and forecasting of data collected from a wireless sensor network. Our approach is considered in the framework of a functional data‐analysis paradigm where observed data is represented in a functional form. To reduce dimensionality, functional principal components analysis is applied to highlight important underlying characteristics and find patterns of variations. The principal scores are modeled with tensor product smooths that allow for smoothing over space and time. The model is then used for simultaneous spatial prediction at unsampled locations and to forecast future observations. We consider soil temperature data from a wireless sensor network of 50 sensor nodes in two different land types (grassland and forest) observed during 60 consecutive days in private property close to Yass, New South Wales, Australia.

Comments

This is the peer-reviewed version of the following article: Lee, D‐J., Z. Zhu, and P. Toscas. "Spatio‐temporal functional data analysis for wireless sensor networks data." Environmetrics 26, no. 5 (2015): 354-362, which has been published in final form at DOI: 10.1002/env.2344. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Posted with permission.

Copyright Owner

John Wiley & Sons, Ltd.

Language

en

File Format

application/pdf

Published Version

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