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.
Citation: Ezekiel NN Nortey, Edmund F. Agyemang, Enoch Sakyi-Yeboah, Obu-Amoah Ampomah, Louis Agyekum. AI meets economics: Can deep learning surpass machine learning and traditional statistical models in inflation time series forecasting?[J]. Data Science in Finance and Economics, 2025, 5(2): 136-155. doi: 10.3934/DSFE.2025007
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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.
| [1] |
Adnan RM, Liang Z, Kuriqi A, et al. (2021) Air temperature prediction using different machine learning models. Indones J Electr Eng Comput Sci 22: 534–541. https://doi.org/10.11591/ijeecs.v22.i1.pp534-541 doi: 10.11591/ijeecs.v22.i1.pp534-541
|
| [2] |
Agyemang EF, Mensah JA, Ocran E, et al. (2023) Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints. Heliyon 9: 1–18. https://doi.org/10.1016/j.heliyon.2023.e22544 doi: 10.1016/j.heliyon.2023.e22544
|
| [3] |
Alomani G, Kayid M, Abd El-Aal MF (2025) Global inflation forecasting and Uncertainty Assessment: Comparing ARIMA with advanced machine learning. J Radiat Res Appl Sci 18: 1–7. https://doi.org/10.1016/j.jrras.2025.101402 doi: 10.1016/j.jrras.2025.101402
|
| [4] |
Aras S, Lisboa PJ (2022) Explainable inflation forecasts by machine learning models. Expert Syst Appl 207: 1–19. https://doi.org/10.1016/j.eswa.2022.117982 doi: 10.1016/j.eswa.2022.117982
|
| [5] |
Araujo GS, Gaglianone WP (2023) Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models. Lat Am J Cent Bank 4: 1–29. https://doi.org/10.1016/j.latcb.2023.100087 doi: 10.1016/j.latcb.2023.100087
|
| [6] |
Arora S, Rani R, Saxena N (2024) A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques. Wires Data Min Knowl 14: 1–27. https://doi.org/10.1002/widm.1536 doi: 10.1002/widm.1536
|
| [7] |
Aygun B, Gunay EK (2021) Comparison of statistical and machine learning algorithms for forecasting daily bitcoin returns. Avrupa Bilim ve Teknoloji Dergisi 21: 444–454. https://doi.org/10.31590/ejosat.822153 doi: 10.31590/ejosat.822153
|
| [8] |
Ben Bouallègue Z, Clare MC, Magnusson L, et al. (2024) The rise of data-driven weather forecasting: A first statistical assessment of machine learning–based weather forecasts in an operational-like context. B Am Meteorol Soc 105: 864–883. https://doi.org/10.1175/BAMS-D-23-0162.1 doi: 10.1175/BAMS-D-23-0162.1
|
| [9] | Bonci A, Kermenov R, Longarini L, et al. (2024) Artificial Intelligence in Manufacturing: A Lightweight Framework for Online Anomaly Detection at the Edge. Commun Ind 2024: 307–311. |
| [10] |
Busari GA, Lim DH (2021) Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance. Comput Chem Eng 155: 1–9. https://doi.org/10.1016/j.compchemeng.2021.107513 doi: 10.1016/j.compchemeng.2021.107513
|
| [11] |
Cen Z, Wang J (2019) Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer. Energy 169: 160–171. https://doi.org/10.1016/j.energy.2018.12.016 doi: 10.1016/j.energy.2018.12.016
|
| [12] | Dong J (2020) Forecasting modeling for China's inflation. Working paper. |
| [13] |
Gunnarsson ES, Isern HR, Kaloudis A, et al. (2024) Prediction of realized volatility and implied volatility indices using AI and machine learning: A review. Int Rev Financ Anal 103221: 1–20. https://doi.org/10.1016/j.irfa.2024.103221 doi: 10.1016/j.irfa.2024.103221
|
| [14] | He QQ, Pang PCI, Si YW (2019) Transfer learning for financial time series forecasting. In PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim International Conference on Artificial Intelligence, Springer International Publishing 1: 24–36. https://doi.org/10.1007/978-3-030-29911-8_3 |
| [15] |
Huang L, Wang J (2018) Global crude oil price prediction and synchronization based accuracy evaluation using random wavelet neural network. Energy 151: 875–888. https://doi.org/10.1016/j.energy.2018.03.099 doi: 10.1016/j.energy.2018.03.099
|
| [16] | Ingabire J, Mung'atu JK (2016) Measuring the performance of autoregressive integrated moving average and vector autoregressive models in forecasting inflation rate in Rwanda. Int J Math Phys Sci Res 4: 15–25. |
| [17] |
Jha N, Tanneru HK, Palla S, et al. (2024) Multivariate analysis and forecasting of the crude oil prices: Part Ⅰ–Classical machine learning approaches. Energy 296: 1–13. https://doi.org/10.1016/j.energy.2024.131185 doi: 10.1016/j.energy.2024.131185
|
| [18] |
Joseph A, Potjagailo G, Chakraborty C, et al. (2024) Forecasting UK inflation bottom up. Int J Forecast 40: 1521–1538. https://doi.org/10.1016/j.ijforecast.2024.01.001 doi: 10.1016/j.ijforecast.2024.01.001
|
| [19] |
Jouilil Y, Iaousse MB (2023) Comparing the accuracy of classical and machine learning methods in time series forecasting: A case study of usa inflation. Stat Optim Inform Comput 11: 1041–1050. https://doi.org/10.19139/soic-2310-5070-1767 doi: 10.19139/soic-2310-5070-1767
|
| [20] |
Kontopoulou VI, Panagopoulos AD, Kakkos I, et al. (2023) A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks. Future Internet 15: 2–31. https://doi.org/10.3390/fi15080255 doi: 10.3390/fi15080255
|
| [21] |
Maccarrone G, Morelli G, Spadaccini S (2021) GDP forecasting: Machine learning, linear or autoregression? Front Artif Intell 4: 1–19. https://doi.org/10.3389/frai.2021.757864 doi: 10.3389/frai.2021.757864
|
| [22] |
Makala D, Li Z (2021) Prediction of gold price with ARIMA and SVM. Journal of Physics: Conference Series 1767: 1–9. https://doi:10.1088/1742-6596/1767/1/012022 doi: 10.1088/1742-6596/1767/1/012022
|
| [23] |
Masini RP, Medeiros MC, Mendes EF (2023) Machine learning advances for time series forecasting. J Econ Surv 37: 76–111. https://doi.org/10.1111/joes.12429 doi: 10.1111/joes.12429
|
| [24] |
Medeiros MC, Vasconcelos GF, Veiga Á, et al. (2021) Forecasting inflation in a data-rich environment: the benefits of machine learning methods. J Bus Econ Stat 39: 98–119. https://doi.org/10.1080/07350015.2019.1637745 doi: 10.1080/07350015.2019.1637745
|
| [25] |
Nortey EN, Agbeli R, Debrah G, et al. (2024) A GARCH-MIDAS approach to modelling stock returns. Commun Stat Appl Met 31: 535–556. https://doi.org/10.29220/CSAM.2024.31.5.535 doi: 10.29220/CSAM.2024.31.5.535
|
| [26] |
Özgür Ö, Akkoç U (2022) Inflation forecasting in an emerging economy: selecting variables with machine learning algorithms. Int J Emerg Mark 17: 1889–1908. https://doi.org/10.1108/IJOEM-05-2020-0577 doi: 10.1108/IJOEM-05-2020-0577
|
| [27] |
Paranhos L (2025) Predicting inflation with recurrent neural networks. Int J Forecast 5283: 1–16. https://doi.org/10.1016/j.ijforecast.2024.07.010 doi: 10.1016/j.ijforecast.2024.07.010
|
| [28] |
Reed SB (2014) One hundred years of price change: The Consumer Price Index and the American inflation experience. Monthly Lab Rev 137: 1–32. https://doi.org/10.21916/mlr.2014.14. doi: 10.21916/mlr.2014.14
|
| [29] |
Riofrío J, Chang O, Revelo-Fuelagán EJ, et al. (2020) Forecasting the Consumer Price Index (CPI) of Ecuador: A comparative study of predictive models. Int J Adv Sci Eng Inf Technol 10: 1078–1084. https://doi.org/10.18517/ijaseit.10.3.10813 doi: 10.18517/ijaseit.10.3.10813
|
| [30] |
Sezer OB, Gudelek MU, Ozbayoglu AM (2020) Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Appl Soft Comput 90: 1–32. https://doi.org/10.1016/j.asoc.2020.106181 doi: 10.1016/j.asoc.2020.106181
|
| [31] |
Valderrama Balaguera JC (2024) Precipitation forecast estimation applying the change point method and ARIMA. Cogent Eng 11: 1–13. https://doi.org/10.1080/23311916.2024.2340191 doi: 10.1080/23311916.2024.2340191
|
| [32] |
Vaughan L, Zhang M, Gu H, et al. (2023) An exploration of challenges associated with machine learning for time series forecasting of COVID-19 community spread using wastewater-based epidemiological data. Sci Total Environ 858: 1–8. https://doi.org/10.1016/j.scitotenv.2022.159748 doi: 10.1016/j.scitotenv.2022.159748
|
| [33] |
Wang J, Qin Z, Hsu J, et al. (2024) A fusion of machine learning algorithms and traditional statistical forecasting models for analyzing American healthcare expenditure. Healthcare Anal 5: 1–10. https://doi.org/10.1016/j.health.2024.100312 doi: 10.1016/j.health.2024.100312
|
| [34] |
Westergaard G, Erden U, Mateo OA, et al. (2024) Time series forecasting utilizing automated machine learning (AutoML): A comparative analysis study on diverse datasets. Information 15: 1–20. https://doi.org/10.3390/info15010039 doi: 10.3390/info15010039
|
| [35] |
Yıldırım S, Tosun E, Çalık A, et al. (2019) Artificial intelligence techniques for the vibration, noise, and emission characteristics of a hydrogen-enriched diesel engine. Energ Source Part A 41: 2194–2206. https://doi.org/10.1080/15567036.2018.1550540 doi: 10.1080/15567036.2018.1550540
|
| [36] |
Zhang X, Qi W, Zhan Z (2021) A Study on Machine-Learning-Based Prediction for Bitcoin's Price via Using LSTM and SVR. Journal of Physics: Conference Series 1732: 1–5. https://doi.org/10.1088/1742-6596/1732/1/012027 doi: 10.1088/1742-6596/1732/1/012027
|
| [37] |
Zhao Y, Li J, Yu L (2017) A deep learning ensemble approach for crude oil price forecasting. Energy Econ 66: 9–16. https://doi.org/10.1016/j.eneco.2017.05.023 doi: 10.1016/j.eneco.2017.05.023
|
| 1. | Analysis of a model for the effects of an external toxin on anaerobic digestion, 2012, 9, 1551-0018, 445, 10.3934/mbe.2012.9.445 | |
| 2. | T. Sari, M.J. Wade, Generalised approach to modelling a three-tiered microbial food-web, 2017, 291, 00255564, 21, 10.1016/j.mbs.2017.07.005 | |
| 3. | Simon M. Stump, Christopher A. Klausmeier, Competition and coexistence between a syntrophic consortium and a metabolic generalist, and its effect on productivity, 2016, 404, 00225193, 348, 10.1016/j.jtbi.2016.06.019 | |
| 4. | Miled El Hajji, How can inter-specific interferences explain coexistence or confirm the competitive exclusion principle in a chemostat?, 2018, 11, 1793-5245, 1850111, 10.1142/S1793524518501115 | |
| 5. | Y. Daoud, N. Abdellatif, T. Sari, J. Harmand, A. Morozov, Steady state analysis of a syntrophic model: the effect of a new input substrate concentration, 2018, 13, 0973-5348, 31, 10.1051/mmnp/2018037 | |
| 6. | Marion Weedermann, Gail S. K. Wolkowicz, Joanna Sasara, Optimal biogas production in a model for anaerobic digestion, 2015, 81, 0924-090X, 1097, 10.1007/s11071-015-2051-z | |
| 7. | Tewfik Sari, Jérôme Harmand, A model of a syntrophic relationship between two microbial species in a chemostat including maintenance, 2016, 275, 00255564, 1, 10.1016/j.mbs.2016.02.008 | |
| 8. | E.I.P. Volcke, M. Sbarciog, E.J.L. Noldus, B. De Baets, M. Loccufier, Steady state multiplicity of two-step biological conversion systems with general kinetics, 2010, 228, 00255564, 160, 10.1016/j.mbs.2010.09.004 | |
| 9. | M.J. Wade, R.W. Pattinson, N.G. Parker, J. Dolfing, Emergent behaviour in a chlorophenol-mineralising three-tiered microbial ‘food web’, 2016, 389, 00225193, 171, 10.1016/j.jtbi.2015.10.032 | |
| 10. | T. Meadows, M. Weedermann, G. S. K. Wolkowicz, Global Analysis of a Simplified Model of Anaerobic Digestion and a New Result for the Chemostat, 2019, 79, 0036-1399, 668, 10.1137/18M1198788 | |
| 11. | The mathematical analysis of a syntrophic relationship between two microbial species in a chemostat, 2012, 9, 1551-0018, 627, 10.3934/mbe.2012.9.627 | |
| 12. | Marco Mauri, Jean-Luc Gouzé, Hidde de Jong, Eugenio Cinquemani, Kiran Raosaheb Patil, Enhanced production of heterologous proteins by a synthetic microbial community: Conditions and trade-offs, 2020, 16, 1553-7358, e1007795, 10.1371/journal.pcbi.1007795 | |
| 13. | Matthew J. Wade, Not Just Numbers: Mathematical Modelling and Its Contribution to Anaerobic Digestion Processes, 2020, 8, 2227-9717, 888, 10.3390/pr8080888 | |
| 14. | Marion Weedermann, Gunog Seo, Gail Wolkowicz, Mathematical model of anaerobic digestion in a chemostat: effects of syntrophy and inhibition, 2013, 7, 1751-3758, 59, 10.1080/17513758.2012.755573 | |
| 15. | Alma Mašić, Hermann J. Eberl, Persistence in a Single Species CSTR Model with Suspended Flocs and Wall Attached Biofilms, 2012, 74, 0092-8240, 1001, 10.1007/s11538-011-9707-8 | |
| 16. | Sarra Nouaoura, Radhouane Fekih-Salem, Nahla Abdellatif, Tewfik Sari, Mathematical analysis of a three-tiered food-web in the chemostat, 2020, 0, 1553-524X, 0, 10.3934/dcdsb.2020369 | |
| 17. | Tahani Mtar, Radhouane Fekih-Salem, Tewfik Sari, Mortality can produce limit cycles in density-dependent models with a predator-prey relationship, 2022, 27, 1531-3492, 7445, 10.3934/dcdsb.2022049 | |
| 18. | Amer Hassan Albargi, Miled El Hajji, Mathematical analysis of a two-tiered microbial food-web model for the anaerobic digestion process, 2023, 20, 1551-0018, 6591, 10.3934/mbe.2023283 | |
| 19. | Abdulrahman Ali Alsolami, Miled El Hajji, Mathematical Analysis of a Bacterial Competition in a Continuous Reactor in the Presence of a Virus, 2023, 11, 2227-7390, 883, 10.3390/math11040883 | |
| 20. | S. Barua, A. Dénes, 2021, Chapter 18, 978-3-030-73240-0, 281, 10.1007/978-3-030-73241-7_18 | |
| 21. | Tewfik Sari, Boumediene Benyahia, The operating diagram for a two-step anaerobic digestion model, 2021, 105, 0924-090X, 2711, 10.1007/s11071-021-06722-7 | |
| 22. | Sarra Nouaoura, Radhouane Fekih-Salem, Nahla Abdellatif, Tewfik Sari, Operating diagrams for a three-tiered microbial food web in the chemostat, 2022, 85, 0303-6812, 10.1007/s00285-022-01812-5 | |
| 23. | Sarra Nouaoura, Nahla Abdellatif, Radhouane Fekih-Salem, Tewfik Sari, Mathematical Analysis of a Three-Tiered Model of Anaerobic Digestion, 2021, 81, 0036-1399, 1264, 10.1137/20M1353897 | |
| 24. | Maximiliano Machado-Higuera, Nohora Meneses-Casas, Andres Hernandez, 2021, Lyapunov Stability Analysis of a Two-Step Anaerobic Digestion Model, 978-1-6654-1883-6, 262, 10.1109/CCAC51819.2021.9633283 | |
| 25. | Hajji ME, How the competitive exclusion principle can be validated using optical density measurements collected on artificially reconstituted soil ecosystems, 2019, 001, 10.17352/ojeb.000009 | |
| 26. | Tewfik Sari, Best Operating Conditions for Biogas Production in Some Simple Anaerobic Digestion Models, 2022, 10, 2227-9717, 258, 10.3390/pr10020258 | |
| 27. | Radhouane Fekih-Salem, Yessmine Daoud, Nahla Abdellatif, Tewfik Sari, A Mathematical Model of Anaerobic Digestion with Syntrophic Relationship, Substrate Inhibition, and Distinct Removal Rates, 2021, 20, 1536-0040, 1621, 10.1137/20M1376480 | |
| 28. | Nour El Houda Zitouni, Mohamed Dellal, Mustapha Lakrib, Substrate inhibition can produce coexistence and limit cycles in the chemostat model with allelopathy, 2023, 87, 0303-6812, 10.1007/s00285-023-01943-3 | |
| 29. | Amer Hassan Albargi, Miled El Hajji, Bacterial Competition in the Presence of a Virus in a Chemostat, 2023, 11, 2227-7390, 3530, 10.3390/math11163530 | |
| 30. | Miled El Hajji, Mathematical modeling for anaerobic digestion under the influence of leachate recirculation, 2023, 8, 2473-6988, 30287, 10.3934/math.20231547 | |
| 31. | Tewfik Sari, Commensalism and syntrophy in the chemostat: a unifying graphical approach, 2024, 9, 2473-6988, 18625, 10.3934/math.2024907 | |
| 32. | Nabil Ben Ali, Nahla Abdellatif, Stability and bifurcations in a model of chemostat with two inter‐connected inhibitions and a negative feedback loop, 2024, 0170-4214, 10.1002/mma.10349 | |
| 33. | Lin Wang, Linlin Bu, Jianhua Wu, Dynamics of a diffusive model in the anaerobic digestion process, 2025, 142, 10075704, 108523, 10.1016/j.cnsns.2024.108523 | |
| 34. | Olympia Roeva, Elena Chorukova, Lyudmila Kabaivanova, Advanced Mathematical Modeling of Hydrogen and Methane Production in a Two-Stage Anaerobic Co-Digestion System, 2025, 13, 2227-7390, 1601, 10.3390/math13101601 |
Figures(5) / Tables(6)
Ezekiel NN Nortey, Edmund F. Agyemang, Enoch Sakyi-Yeboah, Obu-Amoah Ampomah, Louis Agyekum. AI meets economics: Can deep learning surpass machine learning and traditional statistical models in inflation time series forecasting?[J]. Data Science in Finance and Economics, 2025, 5(2): 136-155. doi: 10.3934/DSFE.2025007
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