[1]WU Zhaoyang.Using wavelet transformation and a GM-ARMA model to forecast stock index[J].CAAI Transactions on Intelligent Systems,2011,6(3):279-282.
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Using wavelet transformation and a GM-ARMA model to forecast stock index
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
6
Number of periods:
2011 3
Page number:
279-282
Column:
学术论文—智能系统
Public date:
2011-06-25
- Title:
- Using wavelet transformation and a GM-ARMA model to forecast stock index
- Keywords:
- wavelet decomposition; grey model; ARMA model; GM-ARMA model; stock prediction
- CLC:
- TP18
- DOI:
- -
- Abstract:
- During the process of building a hybrid model by combining wavelet decomposition and other techniques, there is no standard in terms of selecting a wavelet base function and decomposition level. The commonly used ways are usually based on the researcher’s experience or several experiments instead of a quantitative approach. In addition, many hybrid models based on wavelet decomposition do not consider the interaction between sub models. Instead of estimating the parameters in all sub models as the whole, they estimate the parameters separately, which lead to that the prediction result is not optimal. In order to solve this problem, this paper first introduced two new parameters, wavelet functions and decomposition levels, then quantitatively estimated all the parameters as a whole for the purpose of building an optimal hybrid model. For convenience, the model was called the WGM-ARMA model because it combines the wavelet decomposition, grey model, as well as autoregressive integrated moving average (ARMA) model. Experimental results show that the hybrid model significantly reduces prediction errors. As a result, it can be concluded that the model in terms of forecasting stock index is valid and useful, along with the method used to construct the optimal hybrid model.
- References:
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Last Update:
2011-07-23