Stock Index Prediction Method based on ARIMA-ELM Combination Model

Author(s):  
Yi Peng ◽  
Kang He ◽  
Qing Yu
2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Song-shan Yang ◽  
Xiao-hua Yang ◽  
Rong Jiang ◽  
Yi-che Zhang

In order to overcome the inaccuracy of the forecast of a single model, a new optimal weight combination model is established to increase accuracies in precipitation forecasting, in which three forecast submodels based on rank set pair analysis (R-SPA) model, radical basis function (RBF) model and autoregressive model (AR) and one weight optimization model based on improved real-code genetic algorithm (IRGA) are introduced. The new model for forecasting precipitation time series is tested using the annual precipitation data of Beijing, China, from 1978 to 2008. Results indicate the optimal weights were obtained by using genetic algorithm in the new optimal weight combination model. Compared with the results of R-SPA, RBF, and AR models, the new model can improve the forecast accuracy of precipitation in terms of the error sum of squares. The amount of improved precision is 22.6%, 47.4%, 40.6%, respectively. This new forecast method is an extension to the combination prediction method.


2014 ◽  
Vol 644-650 ◽  
pp. 1547-1550
Author(s):  
Wei Xiang Gong ◽  
Guo Chu Chen ◽  
Zhao Hong Feng

A combination model of Theil coefficient and Induce Ordered Weighted Averaging (IOWA) operator based on improved bee colony algorithm is proposed by introducing the Theil coefficient, IOWA arithmetic operators and bee colony algorithm. The model is built from correlation angle, and adopts IOWA arithmetic operators to make the weight coefficient of each model only related to prediction accuracy of the point at every time and has nothing to do with the prediction method. The optimal weight coefficient can be decided through bee colony algorithm, it can be showed that this model can reflect the wind power trend preferably, with which the prediction accuracy can be improved significant


2018 ◽  
pp. 214-223
Author(s):  
AM Faria ◽  
MM Pimenta ◽  
JY Saab Jr. ◽  
S Rodriguez

Wind energy expansion is worldwide followed by various limitations, i.e. land availability, the NIMBY (not in my backyard) attitude, interference on birds migration routes and so on. This undeniable expansion is pushing wind farms near populated areas throughout the years, where noise regulation is more stringent. That demands solutions for the wind turbine (WT) industry, in order to produce quieter WT units. Focusing in the subject of airfoil noise prediction, it can help the assessment and design of quieter wind turbine blades. Considering the airfoil noise as a composition of many sound sources, and in light of the fact that the main noise production mechanisms are the airfoil self-noise and the turbulent inflow (TI) noise, this work is concentrated on the latter. TI noise is classified as an interaction noise, produced by the turbulent inflow, incident on the airfoil leading edge (LE). Theoretical and semi-empirical methods for the TI noise prediction are already available, based on Amiet’s broadband noise theory. Analysis of many TI noise prediction methods is provided by this work in the literature review, as well as the turbulence energy spectrum modeling. This is then followed by comparison of the most reliable TI noise methodologies, qualitatively and quantitatively, with the error estimation, compared to the Ffowcs Williams-Hawkings solution for computational aeroacoustics. Basis for integration of airfoil inflow noise prediction into a wind turbine noise prediction code is the final goal of this work.


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