An Elastic Net Based Algorithm for China Agriculture GDP Prediction

DOI

10.2991/978-94-6463-052-7_96How to use a DOI?

Keywords

GDP prediction; Regression Analysis; Elastic Net; Mean Absolute Error

Abstract

Gross domestic product (GDP) refers to the final result of production activities of all resident units in a country or region within a certain period of time. There are a variety of GDP forecasting methods, which can be classified into three types: Time Series Analysis, Regression Analysis and VAR Model. In our paper, we utilize the agricultural yields data to predict the agriculture GDP, that can be seen as a regression model. We adopt Elastic net linear regression using the penalties from both the lasso and ridge techniques to regularize regression models. We evaluate our result using the metrics of Mean Absolute Error (MAE). The lower MAE, the better performance the model will owns. From the result, Elastic Net method owns the lowest MAE score 2.34. In contrast, the other methods like Linear Regression, Lasso, Ridge and VAR’s MAE are 3.25, 4.25, 3.06, 4.45 respectively.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zihan Qiu
PY  - 2022
DA  - 2022/12/27
TI  - An Elastic Net Based Algorithm for China Agriculture GDP Prediction
BT  - Proceedings of the 2022 International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2022)
PB  - Atlantis Press
SP  - 843
EP  - 849
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-052-7_96
DO  - 10.2991/978-94-6463-052-7_96
ID  - Qiu2022
ER  -