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Forecasting the stock market price index is a challenging task. The exponential smoothing model (ESM), autoregressive integrated moving average model (ARIMA), and the back propagation neural network (BPNN) can be used to make forecasts based on time series. In this paper, a hybrid approach combining ESM, ARIMA, and BPNN is proposed to be the most advantageous of all three models. The weight of the...
This paper proposed a new hybrid forecasting model for the prediction of ozone concentrations in semi-arid area. It is based on chaotic, particle swarm optimization algorithm (CPSO) and back propagation (BP) neural network, called CPSO-BP neural network. The results show that the proposed hybrid model is superior to both the BP neural network and the regression model being tested. The hybrid model...
In the competition paradigm of the electric power markets, both power producers and consumers need some price prediction tools in order to plan their bidding strategies. This paper studies the problem of modeling market clearing price forecasting in deregulated markets. And electricity price forecasting with support vector machines based on artificial fish swarm algorithm is provided. Except considering...
This paper presents a pre-warning system developed to monitor and provide pre-warning to the governmental decision makers in real estate market, applying the grey relational analysis and model design method, with the fuzzy recognition theory based on decision tree and its effectiveness. The real estate early warning system has been designed successfully by the three sub-modules of feature selection...
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