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An accurate prediction of wheat production in advance would give wheat growers, traders, and governmental agencies a great advantage in planning the distribution of wheat for business and consuming purposes. Traditional approach in dealing with such prediction is based on time series analysis through statistical or other intelligent means. These time-series centric methods treat the historical data...
In this paper, a focused time lagged recurrent neural network (FTLRNN) model with gamma memory is developed for multi step ahead (k=1,5,10.20,50,100) prediction of typical Duffing Chaotic time series. It is popularized in Neural network field due to its richness in chaotic behavior. It is observed that duffing time series exhibit a rich chaotic behavior. This paper compares the performance of two...
This work introduces a quantum-inspired intelligent hybrid (QIIH) method for stock market forecasting. It performs a quantum-inspired evolutionary search for the minimum necessary dimension (time lags) embedded in the problem for determining the characteristic phase space that generates the financial time series phenomenon. The proposed QIIH method consists of a quantum-inspired intelligent hybrid...
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