Bridging Temporal Dynamics and Nonlinear Patterns inMacroeconomic Forecasting: A Hybrid Statistical Learning Approach

  • Authors

    • Dharmateja Priyadarshi Uddandarao Sr. Data Scientist - Statistician, Amazon, Seattle, USA
    • Ankush Mahajan Sr. Program Manager, PG & E, San Jose, USA
    • Ravi Kiran Vadlamani Software Development Engineer, Amazon, Seattle, USA
    https://doi.org/10.14419/3e55tj72

    Received date: December 29, 2025

    Accepted date: January 7, 2026

    Published date: January 15, 2026

  • Hybrid Forecasting; Macroeconomic Prediction; Time Series Analysis; Machine Learning Ensemble; ‎Policy-Oriented Forecasting

  • Abstract

    Accurate macroeconomic forecasting is essential for effective policy formulation, financial ‎planning, and economic stability, yet it remains challenging due to structural changes, economic ‎shocks, and complex nonlinear interactions among economic indicators. Traditional time series ‎models often struggle to capture such complexities, while standalone machine learning approaches ‎may overlook temporal dependencies. To address these limitations, this study proposes a Hybrid ‎Predictive Framework for Macroeconomic Forecasting (HPFMF) that integrates complementary ‎strengths of time series and machine learning models. Using annual macroeconomic data for more ‎than 200 countries spanning 2010–2025 from the World Bank Open Data API, the framework ‎applies systematic preprocessing, including missing value handling, winsorization, logarithmic ‎transformation, and feature scaling. A time series hybrid combining ARIMA, Prophet, and ‎Exponential Smoothing captures temporal dynamics and structural shifts, while a stacked machine ‎learning ensemble of Random Forest, XGBoost, and Support Vector Regression models nonlinear ‎interdependencies. These layers are integrated through validation-based weighting to generate ‎robust forecasts. Empirical results show that the proposed framework achieves superior ‎performance, with significant reductions in forecasting errors and an R² of 0.92. Country-wise and ‎temporal validations confirm strong generalizability, demonstrating the framework’s effectiveness ‎for reliable, policy-oriented macroeconomic forecasting‎.

  • References

    1. Xie, H., Xu, X., Yan, F., Qian, X., & Yang, Y. (2024). Deep Learning for Multi-Country GDP Prediction: A Study of Model Performance and Data Impact. arXiv preprint arXiv:2409.02551.
    2. Odhiambo, S. O., Nyakundi, C., & Waititu, H. (2024). Developing a Hybrid ARIMA-XGBOOST Model for Analysing Mobile Money Transaction Data in Kenya. https://doi.org/10.9734/ajpas/2024/v26i10662.
    3. Osman, B. M., & Muse, A. M. S. (2024). Predictive analysis of Somalia’s economic indicators using advanced machine learning models. Cogent Economics & Finance, 12(1), 2426535. https://doi.org/10.1080/23322039.2024.2426535.
    4. Suzuki, A. (2024). Medium‑Term Macroeconomic Forecasting in Ireland: A VAR Setup with Bayesian and Tree Ensemble Models and Forecast Aver-aging. Parliamentary Budget Office, Houses of the Oireachtas, Ireland. Available at:https://data.oireachtas.ie/ie/oireachtas/parliamentaryBudgetOffice/2024/2024-02-27_medium-term-macroeconomic-forecasting-in-ireland-a-var-setup-with-bayesian-and-tree-ensemble-models-and-forecast-averaging_en.pd.
    5. Tang, Z., Xiao, J., & Liu, K. (2025). A novel hybrid deep learning time series forecasting model based on long-short-term patterns. Communications in Statistics-Simulation and Computation, 54(9), 3679-3701. https://doi.org/10.1080/03610918.2024.2362306
    6. Sherly, A., Christo, M. S., & Elizabeth, J. V. (2025). A Hybrid Approach to Time Series Forecasting: Integrating ARIMA and Prophet for Improved Accuracy. Results in Engineering, 105703. https://doi.org/10.1016/j.rineng.2025.105703
    7. Hammam, I. M., El-Kharbotly, A. K., & Sadek, Y. M. (2025). Adaptive demand forecasting framework with a weighted ensemble of regression and machine learning models along life cycle variability. Scientific Reports, 15(1), 38482. https://doi.org/10.1038/s41598-025-23352-w.
    8. Khan, F., Iftikhar, H., Khan, I., Rodrigues, P. C., Alharbi, A. A., & Allohibi, J. (2025). A Hybrid Vector Autoregressive Model for Accurate Macroe-conomic Forecasting: An Application to the US Economy. Mathematics, 13(11), 1706. https://doi.org/10.3390/math13111706.
    9. Nasir, J., Iftikhar, H., Aamir, M., Iftikhar, H., Rodrigues, P. C., & Rehman, M. Z. (2025). A Hybrid LMD–ARIMA–Machine learning framework for enhanced forecasting of financial time series: Evidence from the NASDAQ composite index. Mathematics, 13(15), 2389. https://doi.org/10.3390/math13152389
    10. Aisy, R. R., Zulfa, L., Rahim, Y., & Ahsan, M. (2025). Residual XGBoost regression—Based individual moving range control chart for Gross Do-mestic Product growth monitoring. PLoS One, 20(5), e0321660. https://doi.org/10.1371/journal.pone.0321660.
    11. Maehashi, K., & Shintani, M. (2020). Macroeconomic forecasting using factor models and machine learning: an application to Japan. Journal of the Japanese and International Economies, 58, 101104. https://doi.org/10.1016/j.jjie.2020.101104.
    12. Akbulut, H. (2022). Forecasting inflation in Turkey: A comparison of time-series and machine learning models. Economic Journal of Emerging Mar-kets, 55-71. https://doi.org/10.20885/ejem.vol14.iss1.art5.
    13. Sofianos, E., Alexakis, C., Gogas, P., & Papadimitriou, T. (2025). Machine learning forecasting in the macroeconomic environment: the case of the US output gap. Economic Change and Restructuring, 58(1), 9. https://doi.org/10.1007/s10644-024-09849-w
    14. Babii, A., Ghysels, E., & Striaukas, J. (2022). Machine learning time series regressions with an application to nowcasting. Journal of Business & Eco-nomic Statistics, 40(3), 1094-1106. https://doi.org/10.1080/07350015.2021.1899933.
    15. Mehtab, S., & Sen, J. (2020). A time series analysis-based stock price prediction using machine learning and deep learning models. International jour-nal of business forecasting and marketing intelligence, 6(4), 272-335. https://doi.org/10.1504/IJBFMI.2020.115691
    16. Zheng, H., Wu, J., Song, R., Guo, L., & Xu, Z. (2024). Predicting financial enterprise stocks and economic data trends using machine learning time series analysis. https://doi.org/10.20944/preprints202407.0895.v1.
    17. Zaheer, S., Anjum, N., Hussain, S., Algarni, A. D., Iqbal, J., Bourouis, S., & Ullah, S. S. (2023). A multi-parameter forecasting for stock time series data using LSTM and deep learning model. Mathematics, 11(3), 590. https://doi.org/10.3390/math11030590
    18. Kontopoulou, V. I., Panagopoulos, A. D., Kakkos, I., & Matsopoulos, G. K. (2023). A review of ARIMA vs. machine learning approaches for time series forecasting in data driven networks. Future Internet, 15(8), 255. https://doi.org/10.3390/fi15080255
    19. Sako, K., Mpinda, B. N., & Rodrigues, P. C. (2022). Neural networks for financial time series forecasting. Entropy, 24(5), 657. https://doi.org/10.3390/e24050657.
    20. Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: a survey. Philosophical transactions of the royal society a: mathematical, physical and engineering sciences, 379(2194). https://doi.org/10.1098/rsta.2020.0209.

  • Downloads

  • How to Cite

    Uddandarao , D. P., Mahajan , A. ., & Vadlamani , R. K. . (2026). Bridging Temporal Dynamics and Nonlinear Patterns inMacroeconomic Forecasting: A Hybrid Statistical Learning Approach. International Journal of Accounting and Economics Studies, 13(1), 198-210. https://doi.org/10.14419/3e55tj72