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Assessing predictability of environmental time series with statistical and machine learning models.

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Title: Assessing predictability of environmental time series with statistical and machine learning models.
Authors: Bonas M; Dept. of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA., Datta A; Dept. of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA., Wikle CK; Dept. of Statistics, University of Missouri, Columbia, Missouri, USA., Boone EL; Dept. of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia, USA., Alamri FS; Dept. of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia., Hari BV; Wipro Limited, Bengaluru, India., Kavila I; School of Pure and Applied Physics, Mahatma Gandhi University, Kottayam, India., Simmons SJ; Institute for Advanced Analytics, North Carolina State University, Raleigh, North Carolina, USA., Jarvis SM; Dept. of Mathematics, Trent University, Peterborough, Ontario, Canada., Burr WS; Dept. of Mathematics, Trent University, Peterborough, Ontario, Canada., Pagendam DE; CSIRO Data61, Eveleigh, Brisbane, Australia., Chang W; Div. of Statistics and Data Science, University of Cincinnati, Cincinnati, Ohio, USA., Castruccio S; Dept. of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA.
Source: Environmetrics [Environmetrics] 2025 Jan; Vol. 36 (1). Date of Electronic Publication: 2024 Jul 05.
Publication Type: Journal Article
Journal Info: Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 100968246 Publication Model: Print-Electronic Cited Medium: Print ISSN: 1180-4009 (Print) Linking ISSN: 1099095X NLM ISO Abbreviation: Environmetrics Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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ISSN:1180-4009
DOI:10.1002/env.2864