Hybrid Forecasting Methods—A Systematic Review

Abstract

Time series forecasting has been performed for decades in both science and industry. The forecasting models have evolved steadily over time. Statistical methods have been used for many years and were later complemented by neural network approaches. Currently, hybrid approaches are increasingly presented, aiming to combine both methods’ advantages. These hybrid forecasting methods could lead to more accurate predictions and enhance and improve visual analytics systems for making decisions or for supporting the decision-making process. In this work, we conducted a systematic literature review using the PRISMA methodology and investigated various hybrid forecasting approaches in detail. The exact procedure for searching and filtering and the databases in which we performed the search were documented and supplemented by a PRISMA flow chart. From a total of 1435 results, we included 21 works in this review through various filtering steps and exclusion criteria. We examined these works in detail and collected the quality of the prediction results. We summarized the error values in a table to investigate whether hybrid forecasting approaches deliver better results. We concluded that all investigated hybrid forecasting methods perform better than individual ones. Based on the results of the PRISMA study, the possible applications of hybrid prediction approaches in visual analytics systems for decision making are discussed and illustrated using an exemplary visualization.

Real-Time Ideation Analyzer and Information Recommender

“…Finally, we describe ideation support approaches in which the research gaps are found. We conducted the research found in this section based on the PRISMA methodology [17] in the same manner as illustrated in our previous work [18].…”

An improved multi-attribute group decision-making method for selecting the green supplier of community elderly healthcare service

“…Individual methods include AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), DEA (Data Envelopment Analysis), GRA (Grey Relation Analysis), ANN (Artificial Neural Network), GP (Genetic Programming), LP (Linear Programming), MOP (Multi-objective Programming), CBR (Case-based Reasoning), GA (Genetic Algorithm) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) (Chai et al, 2013;Zolghadr-Asli et al, 2021;Lipovetsky, 2023). Hybrid methods include hybrid AHP, hybrid ANP, and hybrid ANN (Nasseri et al, 2023;Sina et al, 2023). Hybrid fuzzy methods include FTOPSIS (Fuzzy Technique for Order Preference by Similarity to Ideal Solution), FAHP (Fuzzy Analytic Hierarchy Process), FANP (Fuzzy Analytic Network Process), FQFD (Fuzzy Quality Function Deployment), FART (Fuzzy Adaptive Resonance Theory), FST (Fuzzy Sets Theory), LFPP (Logarithmic Fuzzy Preference Programming), and FMOP (Fuzzy Multi-objective Programming) (Hsu et al, 2010;Afrasiabi et al, 2022;Nguyen and Fayek, 2022).…”

Deep Learning Model Effectiveness in Forecasting Limited-Size Aboveground Vegetation Biomass Time Series: Kenyan Grasslands Case Study

“…[28] in crop price prediction, and [29] in health care. More developed neural network approaches have involved hybrid models that combined two or more different types of neural networks-and in some cases also statistical models-to take advantage of the strengths of each model to produce a more powerful and flexible hybrid [30,31]. While beyond the primary scope of this agriculture-focused research, the hybrid CNN-LSTM model exhibits superior performance across diverse disciplines, including finance, environmental engineering, atmospheric sciences [32], and soil sciences [30].…”

“…Our analysis, based on diverse neural network architectures, provides insights into handling dataset complexities. Widely used as benchmarks [14,31], these models enable meaningful comparisons. Focused on a specific problem domain with proven effectiveness [22,37,38], the selected DL models, including MLP for simplicity, LSTM and GRU for sequential modeling, and CNN for spatial dependencies, offer a comprehensive evaluation.…”