Efficient Forecasting of Economic Indicators Using Lightweight Time Series Models in Resource-Constrained Environments

Ahmad Oladunjoye, Precious Akindotuni
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:51-61, 2026.

Abstract

The accurate prediction of economic indicators is essential for decision-making in organisations operating with restricted computational capabilities. This study investigates lightweight time series forecasting models—Naı̈ve Forecast, Exponential Smoothing, ARIMA, and Prophet—applied to predicting exchange rate, GDP growth, inflation rate, and interest rate. Results show that simpler models, particularly Naı̈ve and Exponential Smoothing, achieve competitive accuracy across most indicators while maintaining significantly lower computational cost. This study provides practical insights for deploying efficient forecasting solutions in low-resource settings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-oladunjoye26a, title = {Efficient Forecasting of Economic Indicators Using Lightweight Time Series Models in Resource-Constrained Environments}, author = {Oladunjoye, Ahmad and Akindotuni, Precious}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {51--61}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/oladunjoye26a/oladunjoye26a.pdf}, url = {https://proceedings.mlr.press/v319/oladunjoye26a.html}, abstract = {The accurate prediction of economic indicators is essential for decision-making in organisations operating with restricted computational capabilities. This study investigates lightweight time series forecasting models—Naı̈ve Forecast, Exponential Smoothing, ARIMA, and Prophet—applied to predicting exchange rate, GDP growth, inflation rate, and interest rate. Results show that simpler models, particularly Naı̈ve and Exponential Smoothing, achieve competitive accuracy across most indicators while maintaining significantly lower computational cost. This study provides practical insights for deploying efficient forecasting solutions in low-resource settings.} }
Endnote
%0 Conference Paper %T Efficient Forecasting of Economic Indicators Using Lightweight Time Series Models in Resource-Constrained Environments %A Ahmad Oladunjoye %A Precious Akindotuni %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-oladunjoye26a %I PMLR %P 51--61 %U https://proceedings.mlr.press/v319/oladunjoye26a.html %V 319 %X The accurate prediction of economic indicators is essential for decision-making in organisations operating with restricted computational capabilities. This study investigates lightweight time series forecasting models—Naı̈ve Forecast, Exponential Smoothing, ARIMA, and Prophet—applied to predicting exchange rate, GDP growth, inflation rate, and interest rate. Results show that simpler models, particularly Naı̈ve and Exponential Smoothing, achieve competitive accuracy across most indicators while maintaining significantly lower computational cost. This study provides practical insights for deploying efficient forecasting solutions in low-resource settings.
APA
Oladunjoye, A. & Akindotuni, P.. (2026). Efficient Forecasting of Economic Indicators Using Lightweight Time Series Models in Resource-Constrained Environments. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:51-61 Available from https://proceedings.mlr.press/v319/oladunjoye26a.html.

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