Open AccessInterval forecasting model for time series based on the fuzzy clustering technique
Author(s) -
Tai Vovan,
Dinh Phamtoan
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1109/1/012030
Subject(s) - fuzzy logic , series (stratigraphy) , time series , data mining , matlab , computer science , cluster analysis , fuzzy clustering , interval (graph theory) , statistics , mathematics , machine learning , artificial intelligence , paleontology , combinatorics , biology , operating system
This paper proposes the forecasting model for the fuzzy time series based on the improvement of the background data and fuzzy relationship (IFTC). This algorithm is built based on the fuzzy cluster analysis which the suitable number of clusters for series is considered. The problem of interpolating data according to fuzzy relationships of time series in the trapezoidal fuzzy number is also established. The proposed model is illustrated step by step by a numerical example and effectively implemented by the Matlab procedure. The IFCT has advantages in comparing to other models via the several indexes such as the MAE, MAPE and MSE with the Enrollment dataset.