Hybrid forecasting methods across varied domains-a systematic review - Xhabafti, Malvina; Sinaj, Valentina (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
Time series forecasting is one of the links that has developed since early times due to risk management, efficient allocation of resources, performance evaluation, strategic planning, and the formulation of effective policies for individuals, organizations, and societies. Forecasting models have evolved steadily by hybridizing statistical and neural network techniques ensuring efficiency and accurate predictions. In this paper, a systematic review of the literature was made through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, highlighting the domains that mostly use hybrid techniques by defining the ones with the highest frequency of implementation in each domain we predefined. During the selection process from the 4 selected databases, 2251 works were taken into consideration, of which 25 were the ones that were included in the review process through various filtering steps and exclusion criteria. Ongoing, we defined four main categories where we presented each paper individually by briefly explaining the underlying data, the proposed hybrid forecasting approach and the evaluation performance metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). In a summary table, we highlight the most used hybrid methods for each domain, concluding which of the statistical and deep learning methods are mostly applied in the specified domains.