@article{SSB*23,

title = {Hybrid Forecasting Methods - A Systematic Review},

author = {Lennart B. Sina and Cristian A. Secco and Midhad Blazevic and Kawa Nazemi},

url = {https://www.mdpi.com/2079-9292/12/9/2019},

doi = {10.3390/electronics12092019},

year  = {2023},

date = {2023-04-27},

urldate = {2023-04-27},

journal = {Electronics},

volume = {12},

number = {7},

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.},

keywords = {hybrid forecsting, PRISMA study},

pubstate = {published},

tppubtype = {article}

}