How to Identify If Your Time Series Inputs Are Adequate for AI Applications: Assessing Minimum Data Requirements in Environmental Analyses

Eduart Murcia and Sandra M. Guzmán

Release Date: 1/10/2024

DOI: 10.32473/edis-ae594-2024

Agriculture

Abstract

This publication is intended for scientists, technicians, and decision-makers who want to start using machine learning (ML) in their projects. It provides an overview of the factors that should be considered when employing ML applications with time series (TS) data as input. Written by Eduart Murcia and Sandra M. Guzmán, and published by the UF/IFAS Department of Agricultural and Biological Engineering, January 2024.

About this Publication

This document is AE594, one of a series of the Department of Agricultural and Biological Engineering, UF/IFAS Extension. Original publication date January 2024. Visit the EDIS website at https://edis.ifas.ufl.edu for the currently supported version of this publication.

About the Authors

Eduart Murcia, graduate research assistant; and Sandra M. Guzmán, Ph.D., assistant professor, water resources; Department of Agricultural and Biological Engineering; UF/IFAS Indian River Research and Education Center, Fort Pierce, FL 34945.

How to Cite

Murcia Botache, E., & Guzman, S. (2024). How to Identify If Your Time Series Inputs Are Adequate for AI Applications: Assessing Minimum Data Requirements in Environmental Analyses. EDIS, 2024(1). https://doi.org/10.32473/edis-ae594-2024

Authors

Eduart Murcia Botache

student

University of Florida

graduate research assistant, UF/IFAS Indian River Research and Education Center, Fort Pierce, FL

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Sandra Guzmán

Contact

Specialist

University of Florida

associate professor, water resources, Department of Agricultural and Biological Engineering, UF/IFAS Indian River Research and Education Center, Fort Pierce, FL

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Publication #AE594