School of Mathematical and Statistical Sciences Faculty Publications and Presentations
Document Type
Article
Publication Date
4-22-2025
Abstract
This study examined the forecasting ability of deep learning (DL) and machine learning (ML) models against benchmark traditional statistical models for the monthly inflation rates in the USA. The study compared various DL and ML models like transformers, linear regression, gradient boosting (GB), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost) with traditional baseline time-series models like autoregressive integrated moving averages (ARIMA) and exponential smoothing (ETS) with Holt-Winters seasonal method utilizing data sourced from the Federal Reserve Bank of St. Louis. The study consistently showed that all DL and ML models outperformed the traditional approaches. In particular, the Transformer (RMSE = 0.0291, MAE = 0.0221) exhibited the lowest error rates, suggesting high forecasting accuracy for the monthly inflation data. In contrast, ARIMA (RMSE = 0.2038, MAE = 0.1895) and ETS (RMSE = 0.1619, MAE = 0.1455) models displayed higher error rates. Due to the ability of DL and ML models to process large volumes of data and identify underlying patterns, they are particularly effective for dynamic and evolving datasets. This study explored how novel DL modeling techniques could augment the accuracy and reliability of economic forecasting. The results indicate that policymakers and economists could leverage DL and ML models to gain deeper insights into inflation dynamics and implement more informed strategies to combat inflationary pressure. Hybrid methods should be further investigated in future studies to enhance economic forecasting accuracy.
Recommended Citation
Nortey, Ezekiel NN, Edmund F. Agyemang, Enoch Sakyi-Yeboah, Obu-Amoah Ampomah, and Louis Agyekum. "AI meets economics: Can deep learning surpass machine learning and traditional statistical models in inflation time series forecasting?." Data Science in Finance and Economics 5, no. 2 (2025): 136-155. https://doi.org/10.3934/DSFE.2025007
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Data Science in Finance and Economics
DOI
10.3934/DSFE.2025007
Comments
Student publication.
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)