Open AccessForecasting retail sales based on cheng fuzzy time series and particle swarm optimization clustering algorithm
Author(s) -
Riki Ariyanto,
R. Heru Tjahjana,
Titi Udjiani
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1918/4/042032
Subject(s) - particle swarm optimization , cluster analysis , mean squared error , interval (graph theory) , series (stratigraphy) , computer science , data mining , fuzzy logic , algorithm , time series , value (mathematics) , mathematical optimization , cluster (spacecraft) , set (abstract data type) , data set , mathematics , statistics , artificial intelligence , machine learning , paleontology , combinatorics , biology , programming language
Use of the conventional forecasting method, which is based on trend data with average sales in the last few months, results inaccurate forecasting due to a large difference in data, this is the same as fuzzy forecasting with the same interval length or static. Therefore, this paper recommends using the Cheng forecasting method combined with the Particle Swarm Optimization (PSO) algorithm. We use an artificial intelligence, i.e., PSO algorithm to set non-static length of intervals each cluster on Cheng method. The comparison of this method yields a better root mean square error (RMSE) value for each cluster on the recommended method.