Journal of Intelligent Computing Hajar Ait Lamkademe 17 2 2026 https://doi.org/10.6025/jic/2026/17/2/43-58 https://www.dline.info/jic/fulltext/v17n2/jicv17n2_1.pdf 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.