research-article

Philippine Economic Growth: GDP Prediction using Machine Learning Algorithms

Authors:

Sherrydan Laygo-Matsumoto,

Mary Jane SamonteAuthors Info & Claims

ICCBD '21: Proceedings of the 2021 4th International Conference on Computing and Big Data

Pages 15 - 20

https://doi.org/10.1145/3507524.3507526

Published: 08 March 2022 Publication History

Abstract

The study focused on Gross Domestic Product (GDP) prediction for the Philippines using macroeconomic indicators. Gross Domestic Product represents the economy's aggregate monetary value for its final goods and services manufactured and marketed inside the market. It is one of the critical measures of an economy's economic growth or contraction, which is used globally in analyzing the relative size of an economy and how well a country is performing relative to other economies. In this study, GDP prediction using machine learning algorithms utilizing macroeconomic indicators as predictive variables was explored. The significance and association of these variables to GDP were probed with machine learning predictive algorithm models. The data had gone through Spearman's rank feature selection methodology to choose the most appropriate variables used in the modeling. Based on the results of the ML algorithms, Gradient Boosting Machine yielded the least RSME and was the best model with 2.09% and an RMSLE of 4%. Deep learning and distributed random forest also provided considerably good results. It is concluded that macroeconomic indicators have significant relationships with GDP, and we can employ machine learning algorithms in GDP prediction to demonstrate that.

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Cited By

Shams MTarek ZEl-kenawy EEid MElshewey A(2024)Predicting Gross Domestic Product (GDP) using a PC-LSTM-RNN model in urban profiling areasComputational Urban Science10.1007/s43762-024-00116-24:1Online publication date: 29-Jan-2024
https://doi.org/10.1007/s43762-024-00116-2
Kumar SSaran KGarg YDubey GGoel SJha AVerma A(2024)A novel influence quantification model on Instagram using data science approach for targeted business advertising and better digital marketing outcomesSocial Network Analysis and Mining10.1007/s13278-024-01230-z14:1Online publication date: 27-Mar-2024
https://doi.org/10.1007/s13278-024-01230-z
Tran CPhan TThi-Ngoc-Diem PNguyen H(2023)Gross Domestic Product Prediction in Various Countries with Classic Machine Learning TechniquesNature of Computation and Communication10.1007/978-3-031-28790-9_9(136-147)Online publication date: 24-Mar-2023
https://doi.org/10.1007/978-3-031-28790-9_9

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ICCBD '21: Proceedings of the 2021 4th International Conference on Computing and Big Data

November 2021

148 pages

ISBN:9781450387194

DOI:10.1145/3507524

Copyright © 2021 ACM.

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Published: 08 March 2022

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ICCBD 2021: 2021 4th International Conference on Computing and Big Data

November 27 - 29, 2021

Wuhan, China

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Cited By

Shams MTarek ZEl-kenawy EEid MElshewey A(2024)Predicting Gross Domestic Product (GDP) using a PC-LSTM-RNN model in urban profiling areasComputational Urban Science10.1007/s43762-024-00116-24:1Online publication date: 29-Jan-2024
https://doi.org/10.1007/s43762-024-00116-2
Kumar SSaran KGarg YDubey GGoel SJha AVerma A(2024)A novel influence quantification model on Instagram using data science approach for targeted business advertising and better digital marketing outcomesSocial Network Analysis and Mining10.1007/s13278-024-01230-z14:1Online publication date: 27-Mar-2024
https://doi.org/10.1007/s13278-024-01230-z
Tran CPhan TThi-Ngoc-Diem PNguyen H(2023)Gross Domestic Product Prediction in Various Countries with Classic Machine Learning TechniquesNature of Computation and Communication10.1007/978-3-031-28790-9_9(136-147)Online publication date: 24-Mar-2023
https://doi.org/10.1007/978-3-031-28790-9_9

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