NONE 20260422103818843 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON BUSINESS AND ECONOMICS 1109-9526 2224-2899 Henri Kasongo Shabani Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF CONGO Kasra Mortazavi Institute of Smart Systems Technologies, Alpen-Adria-Universität Klagenfurt, Universitätsstraße 65-67, 9020 Klagenfurt, AUSTRIA Witesyavwirwa Vianney Kambale Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria, SOUTH AFRICA Mahmoud Hamed Institute of Smart Systems Technologies, Alpen-Adria-Universität Klagenfurt, Universitätsstraße 65-67, 9020 Klagenfurt, AUSTRIA Samuel Matia Kangoni Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF CONGO Kyandoghere Kyamakya Faculté Polytechnique, Université de Kinshasa (UNIKIN), Kinshasa, DEMOCRATIC REPUBLIC OF CONGO Forecasting macro- and microeconomic trends is especially difficult in data-scarce settings such as the Democratic Republic of the Congo (DRC). Standard deep learning models, LSTMs, temporal CNNs, and Transformers typically require clean, synchronized time-series data and extensive GPU training. Yet, they offer limited transparency to central bank analysts and policymakers. We propose dCNN-E(ASP), a fully analytical neuro-symbolic framework that combines a binary Cellular Neural Network reservoir with a self-growing Answer Set Programming rulebook. Training requires only two closed-form matrix inversions, enabling realtime inference on a $30 Raspberry Pi and robustness to missing or noisy data. Across three Congolese applications, headline inflation, hydropower grid balancing, and cross-border copper flows retrospective backtests achieve 12–20% lower MAPE than tuned benchmarks. 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