Application of Machine Learning Algorithms for Univariate Time Series Analysis in Agricultural Forecasting: A Case Study of Cherry Production in Turkey

The full text of the chapter related to this study can be accessed via the following link: Research Gate Repository

Please cite this study if you use it in your research as follows:

Duman, H. (2024). Application Of Machine Learning Algorithms For Univariate Time Series Analysis In Agricultural Forecasting: A Case Study Of Cherry Production In Turkey. In N. Köleoğlu, Ş. Çelik, & M. Gülşen İrteş (Eds.), Ekonomi̇, Sosyal ve Beşeri̇ Bi̇li̇mlerde İstati̇sti̇ksel Araştırmalar (pp. 163–202). Holistence.

Abstract

Keywords:

R Packages

This study used the R statistical environment, version 4.2.2, developed by R Core Team (2022). The tidyverse meta-package, version 2.0.0, created Wickham et al. (2019), was employed for data manipulation and cleaning. For time series data extension, the tsibble package (version 1.1.3), developed by Wang, Cook, & Hyndman (2020), was utilized. To build forecasting models, the fable package (version 0.3.3) created by O’Hara-Wild, Hyndman, & Wang (2023a) was employed. For feature extraction and statistical analysis, the feasts package (version 0.3.1), developed by O’Hara-Wild, Hyndman, & Wang (2023b), was utilized. To create world maps, rnaturalearth version 0.3.4 by Massicotte & South (2023), rnaturalearthdata version 0.1.0 by South (2017),sf package version 1.0.14 and sp package version 2.1.2 with contributions from Pebesma & Bivand (2005) and Bivand, Pebesma, & Gomez-Rubio (2013), Pebesma (2018) were employed.

Acknowledgements

This analysis adapted and modified code from various sources, such as books, package manuals, vignettes, and GitHub repositories. The sources are cited as follows:

• Data preparing, manipulation, cleaning, and visualization: Wickham et al. (2019), Wang et al. (2020), Wang & contibutors (2024),
• Map Visualization: Massicotte & South (2023), South (2017), Pebesma & Bivand (2005), Bivand et al. (2013), Pebesma & contibutors (2024)
• Training and Forecasting Models: Kuhn & Silge (2022), Kuhn & Wickham (2020), Kuhn & Wickham (2024), Dancho & Vaughan (2024), Dancho (2024), Hyndman, Koehler, Ord, & Snyder (2008), Eyduran, Ertürk, Duman, & Aliyev (2020)

Code References

Bivand, R. S., Pebesma, E. J., & Gomez-Rubio, V. (2013). Applied spatial data analysis with R, second edition. Springer, NY. https://asdar-book.org/

Dancho, M. (2024). Modeltime: The tidymodels extension for time series modeling. https://github.com/business-science/modeltime R package version 1.3.0, https://business-science.github.io/modeltime/

Dancho, M., & Vaughan, D. (2024). Timetk: A tool kit for working with time series. https://github.com/business-science/timetk R package version 2.9.0, https://business-science.github.io/timetk/

Eyduran, E., Ertürk, Y. E., Duman, H., & Aliyev, P. (2020). Examples of univariate time series analysis with artificial neural networks in r. https://doi.org/10.13140/RG.2.2.36747.31528/1

Hyndman, R. J., Koehler, A., Ord, K., & Snyder, R. (2008). Forecasting with exponential smoothing: The state space approach (p. 359). Berlin: Springer.

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O’Hara-Wild, M., Hyndman, R., & Wang, E. (2023a). Fable: Forecasting models for tidy time series. https://CRAN.R-project.org/package=fable R package version 0.3.3

O’Hara-Wild, M., Hyndman, R., & Wang, E. (2023b). Feasts: Feature extraction and statistics for time series. https://CRAN.R-project.org/package=feasts R package version 0.3.1

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