{"id":25724,"date":"2026-09-03T11:54:34","date_gmt":"2026-09-03T11:54:34","guid":{"rendered":"https:\/\/scientificassociation.org\/?post_type=journal-paper&#038;p=25724"},"modified":"2026-09-03T11:54:34","modified_gmt":"2026-09-03T11:54:34","slug":"forecasting-food-inflation-in-africa-a-comparison-of-hybrid-models","status":"publish","type":"journal-paper","link":"https:\/\/scientificassociation.org\/ar\/journal-paper\/forecasting-food-inflation-in-africa-a-comparison-of-hybrid-models\/","title":{"rendered":"Forecasting Food Inflation in Africa: A Comparison of Hybrid Models"},"content":{"rendered":"<div class=\"padding_abstract justify ltr\">This study investigates hybrid models for forecasting food inflation rates across six African countries: Nigeria, South Africa, Cape Verde, Morocco, Botswana, and Libya. The stacking ensemble method with Random Forest (RF) as the meta-learner was utilized as a combination technique. For robustness, a sensitivity analysis was conducted by interchanging Random Forest with Support Vector Regression (SVR), and multistep forecasts were evaluated at 3, 6, and 12-month forecast horizons. Out-of-sample forecast results reveal that different hybrid models perform optimally across countries: ARIMA-PROPHET in Nigeria and Morocco, ETS-SVR in South Africa and Botswana, SVR-MLP in Cape Verde, and NNAR-PROPHET in Libya. The results of the meta-learner sensitivity analysis indicate model changes in the countries, except Botswana and Libya, where original hybrids remained optimal. Multistep forecasts further affirm the superiority of NNAR-PROPHET in South Africa and Cape Verde, and PROPHET-MLP in Morocco for longer-term forecasts, while in Nigeria, Botswana, and Libya, hybrid models combining classical and machine learning techniques performed best across all horizons.<\/div>\n","protected":false},"featured_media":25680,"template":"","meta":{"_acf_changed":false},"journal-name":[219],"paper-tag":[273],"class_list":["post-25724","journal-paper","type-journal-paper","status-publish","has-post-thumbnail","hentry","journal-name-jcese","paper-tag--5--3--2026"],"acf":[],"_links":{"self":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper\/25724","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-paper"}],"about":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/types\/journal-paper"}],"wp:attachment":[{"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/media?parent=25724"}],"wp:term":[{"taxonomy":"journal-name","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/journal-name?post=25724"},{"taxonomy":"paper-tag","embeddable":true,"href":"https:\/\/scientificassociation.org\/ar\/wp-json\/wp\/v2\/paper-tag?post=25724"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}