Explainable Artificial Intelligence for Inflation Forecasting with SHAP, Random Forest, and LSTM: An Application to Algeria

DOI

10.2991/978-94-6239-711-8_8How to use a DOI?

Keywords

Inflation forecasting; Random Forest; Explainable AI; Economic indicators; LSTM Time-series analysis

Abstract

Accurate inflation forecasting is crucial for effective policymaking, yet inflation dynamics often lack transparency. This study applies Explainable Artificial Intelligence (XAI) to analyze inflation in Algeria by combining SHapley Additive exPlanations (SHAP) with Random Forest (RF) and Long Short-Term Memory (LSTM) models. While LSTM better captures extreme inflation episodes and RF provides smoother forecasts, the main contribution lies in explaining model predictions. SHAP results identify food inflation as the dominant driver, followed by lagged producer inflation and the GDP deflator, revealing strong nonlinear and temporal effects, whereas energy inflation plays a limited role. In addition, the analysis highlights how machine-learning models can complement traditional econometric approaches by capturing complex interactions and regime-dependent behaviors that are difficult to observe with linear frameworks. Overall, integrating ML models with XAI enhances transparency, supports informed policy decisions, and provides robust, interpretable evidence to better understand and manage inflationary pressures in Algeria’s evolving macroeconomic environment context effectively.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Nouara Boudouh
AU  - Bilal Mokhtari
AU  - Sihem Kerdoudi
PY  - 2026
DA  - 2026/06/24
TI  - Explainable Artificial Intelligence for Inflation Forecasting with SHAP, Random Forest, and LSTM: An Application to Algeria
BT  - Proceedings of the International Conference on Artificial Intelligence Applications in Business Administration in MENA Region (ICAIABA 2026)
PB  - Atlantis Press
SP  - 67
EP  - 77
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-711-8_8
DO  - 10.2991/978-94-6239-711-8_8
ID  - Boudouh2026
ER  -