Hybrid forecasting model-based data mining and genetic algorithm-adaptive particle swarm optimisation: a case study of wind speed time series

2016 ◽  
Vol 10 (3) ◽  
pp. 287-298 ◽  
Author(s):  
Jianzhou Wang ◽  
Fanyong Zhang ◽  
Feng Liu ◽  
Jianjun Ma
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Time Series ◽  
Wind Speed ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Forecasting Model ◽  
Model Based ◽  
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.

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