en و موضوعات مربوط Other Related Subject پژوهشي Research <p>  Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly efficient and a new evolutionary computation technique inspired by birds’ flight and communication behaviors. The proposed algorithm determines the length of each interval in the universe of discourse and degree of membership values, simultaneously. Two numerical data sets are selected to illustrate the proposed method and compare the forecasting accuracy with four fuzzy time series methods. The results indicate that the proposed algorithm satisfactorily competes well with similar approaches. </p> Forecasting, Fuzzy Time Series, Time Variant, Particle Swarm Optimization. 269 276 http://ijiepr.iust.ac.ir/browse.php?a_code=A-10-10-3&slc_lang=en&sid=1 Mehdi Mahnam 180031947532846002058 180031947532846002058 No Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran Seyyed Mohammad Taghi Fatemi Ghomi fatemi@aut.ac.ir 180031947532846002059 180031947532846002059 Yes Professor of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, Tehran, Iran