How to Identify If Your Time Series Inputs Are Adequate for AI Applications: Assessing Minimum Data Requirements in Environmental Analyses
Hand on a computer keyboard. Photo taken 10-27-21. UF/IFAS Photo by Tyler Jones.
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Keywords

Machine Learning
agriculture data
data collection
data

Categories

How to Cite

Murcia Botache, Eduart, and Sandra Guzman. 2024. “How to Identify If Your Time Series Inputs Are Adequate for AI Applications: Assessing Minimum Data Requirements in Environmental Analyses: AE594, 1/2024”. EDIS 2024 (1). Gainesville, FL. https://doi.org/10.32473/edis-ae594-2024.

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.

https://doi.org/10.32473/edis-ae594-2024
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PDF-2024

References

Brownlee, J. 2018. How to Develop a Skillful Machine Learning Time Series Forecasting Model. 1–16. https://machinelearningmastery.com/how-to-develop-a-skilful-time-series-forecasting-model/

Brownlee, J. 2020. Introduction to Time Series Forecasting with Python.

Brownlee, J. 2022. “How Much Data Is Required for Machine Learning?” Postindustria. https://postindustria.com/how-much-data-is-required-for-machine-learning/

Cerqueira, V., L. Torgo, and C. Soares. 2019. Machine Learning vs. Statistical Methods for Time Series Forecasting: Size Matters. http://arxiv.org/abs/1909.13316

Um, T. T., F. M. J. Pfister, D. Pichler, S. Endo, M. Lang, S. Hirche, U. Fietzek, and D. Kulic. 2017. “Data Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring Using Convolutional Neural Networks.” ICMI ’17 - Proceedings of the 19th ACM International Conference on Multimodal Interaction:216–220. https://doi.org/10.1145/3136755.3136817

Wen, Q., L. Sun, F. Yang, X. Song, J. Gao, X. Wang, and H. Xu. 2021. “Time Series Data Augmentation for Deep Learning: A Survey.” IJCAI International Joint Conference on Artificial Intelligence:4653–4660. https://doi.org/10.24963/ijcai.2021/631

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