@article{4739,
  author = {Hajar Ait Lamkademe},
  title = {Capturing Nonlinear Dynamics in Global Debt Markets: An ARIMA-LSTM Comparative Study},
  journal = {Journal of Intelligent Computing},
  year = {2026},
  volume = {17},
  number = {2},
  doi = {https://doi.org/10.6025/jic/2026/17/2/43-58},
  url = {https://www.dline.info/jic/fulltext/v17n2/jicv17n2_1.pdf},
  abstract = {Modeling the dynamics of global debt markets requires frameworks capable of capturing nonlinearity, nonstationarity,
and structural regime shifts inherent in macro-financial time series. This study presents a rigorous
comparative analysis of linear econometric and nonlinear deep learning models, specifically ARIMA,
SARIMA, and Long Short-Term Memory (LSTM) networks, to evaluate their effectiveness in forecasting
global debt issuance. Using a quarterly dataset from the Bank for International Settlements spanning 1962-
2025, the analysis integrates comprehensive preprocessing, including stationarity transformations, seasonal
decomposition, and feature scaling, to ensure methodological robustness.
Empirical results reveal that global debt issuance exhibits strong persistence, pronounced quarterly
seasonality, and nonlinear responses to macroeconomic shocks, particularly during the Global Financial
Crisis and the COVID-19 period. While ARIMA adequately captures linear dependencies under stationarity
assumptions, it fails to model seasonal and nonlinear structures. SARIMA improves performance by explicitly
incorporating seasonal autoregressive and moving average components, yielding statistically robust residual
diagnostics consistent with white-noise processes. However, LSTM significantly outperforms both models
by learning complex temporal dependencies and nonlinear interactions without requiring explicit model
specification.
Model evaluation using MAE, RMSE, MSE, and MAPE consistently demonstrates the superior predictive
accuracy and adaptability of LSTM, particularly under structural instability and regime shifts. The findings
underscore the limitations of purely linear frameworks and highlight the efficacy of deep learning
architectures in financial time-series forecasting. Furthermore, the results support the adoption of hybrid
modeling strategies that integrate econometric interpretability with machine learning flexibility, offering a
robust paradigm for analyzing and forecasting complex global debt dynamics.},
}