scholarly journals Improved Fuzzy Time Series Forecasting Model Based on Optimal Lengths of Intervals Using Hedge Algebras and Particle Swarm Optimization

2021 ◽  
Vol 6 (1) ◽  
pp. 1286-1297
Author(s):  
Nghiem Van Tinh ◽  
Nguyen Cong Dieu ◽  
Nguyen Tien Duy ◽  
Tran Thi Thanh
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Swarm Optimization ◽  
Model Based

A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization

2011 ◽  
Vol 38 (7) ◽  
pp. 8014-8023 ◽  
Author(s):  
Yao-Lin Huang ◽  
Shi-Jinn Horng ◽  
Mingxing He ◽  
Pingzhi Fan ◽  
Tzong-Wann Kao ◽  
...  
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Swarm Optimization ◽  
Hybrid Forecasting

scholarly journals An ARMA Type Fuzzy Time Series Forecasting Method Based on Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Erol Egrioglu ◽  
Ufuk Yolcu ◽  
Cagdas Hakan Aladag ◽  
Cem Kocak
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Moving Average ◽  
Particle Swarm ◽  
Time Series Forecasting ◽  
Fuzzy Time Series ◽  
Time Series Models ◽  
Swarm Optimization ◽  
Accurate Forecast ◽  
Time Invariant

In the literature, fuzzy time series forecasting models generally include fuzzy lagged variables. Thus, these fuzzy time series models have only autoregressive structure. Using such fuzzy time series models can cause modeling error and bad forecasting performance like in conventional time series analysis. To overcome these problems, a new first-order fuzzy time series which forecasting approach including both autoregressive and moving average structures is proposed in this study. Also, the proposed model is a time invariant model and based on particle swarm optimization heuristic. To show the applicability of the proposed approach, some methods were applied to five time series which were also forecasted using the proposed method. Then, the obtained results were compared to those obtained from other methods available in the literature. It was observed that the most accurate forecast was obtained when the proposed approach was employed.

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