A Hybrid Forecasting Model Based on Adaptive Fuzzy Time Series and Particle Swarm Optimization

2012 ◽  
Author(s):  
Yao-Lin Huang ◽  
Shi-Jinn Horng ◽  
Tzong-Wang Kao ◽  
I-Hong Kuo ◽  
Terano Takao
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Time Series ◽  
Forecasting Model ◽  
Swarm Optimization ◽  
Adaptive Fuzzy ◽  
Model Based ◽  
Hybrid Forecasting

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

A Hybrid Forecasting Model for Non-Stationary Time Series

2012 ◽  
Vol 3 (2) ◽  
pp. 67-82 ◽  
Author(s):  
Yi Xiao ◽  
Jin Xiao ◽  
Shouyang Wang
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Stationary Time Series ◽  
Adaptive Mutation ◽  
Forecasting Model ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Individual Fitness ◽  
Hybrid Forecasting

In time series analysis, an important problem is how to extract the information hidden in the non-stationary and noise data and combine it into a model for forecasting. In this paper, the authors propose a TEI@I based hybrid forecasting model. A novel feed forward neural network is developed based on the improved particle swarm optimization with adaptive genetic operator (IPSO-FNN) for forecasting. In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Subsequently, a crossover rate which only depends on generation and an adaptive mutation rate based on individual fitness are designed. The parameters of FNN are optimized by binary and decimal particle swarm optimization. Further, the forecast results of IPSO-FNN are adjusted with the knowledge from text mining and an expert system. The empirical results on the container throughput forecast of Tianjin Port show that forecasts with the proposed method are much better than some other methods.

An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization

2009 ◽  
Vol 36 (3) ◽  
pp. 6108-6117 ◽  
Author(s):  
I-Hong Kuo ◽  
Shi-Jinn Horng ◽  
Tzong-Wann Kao ◽  
Tsung-Lieh Lin ◽  
Cheng-Ling Lee ◽  
...  
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Time Series ◽  
Improved Method ◽  
Swarm Optimization
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