چکیده مقاله
Time series forecasting is still an open problem in data mining One major method that is dealing for this problem is Back propagation BP neural networks BPs are very stable and usually converge to a final state But they are usually trained slowly and need large pattern numbers or resources, specially, when the patterns of time series are very complex In this paper Complex valued neural network CVNN is suggested A CVNN is a neural network that all input, output and weight values are complex numbers The functionality of CVNN is higher than traditional feed forward neural networks So it can be used instead of them in some real problems to get better or faster responses In this paper Fourier transform is applied on times series data to get phase encoded input values Then left and the most important parts of its respond are used to learn the CVNN The proposed method in this paper has less neurons number than CVNN In addition network efficiency is better and it would be adopted faster than CVNN In addition, three lemmas have been proved which define how to select ongoing window size and normalization coefficients At last, the advantage of the proposed method is compared as three different cases; noisy and noiseless Mackey glass time series, an ecological dataset and a weather dataset
نویسندگان
Reza Askari Moghadam
Faculty of New Sciences and Technologies, University of Tehran, Tehran
Ali Sohrabi
Faculty of New Sciences and Technologies, University of Tehran, Tehran
شیوه ارجاع
Askari Moghadam, Reza and Sohrabi, Ali,1394,Real valued Time Series Prediction by Complex Valued Neural Network and Normalized Fourier Transformed Data,International Conference on New Research Findings in Electrical Engineering and Computer Science,Tehran
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