About One Method of Learning Sample Generation and Normalization for Time Series Extrapolation Problem in The Absence of a Priori Information about Size of Changing of Its Values in Future
Methods of learning sample generation and normalization for the case of absence of a priori information about size of changing of time series values on extrapolation interval is proposed, that is time series can arbitrarily increase and decrease. Each example of learning sample is normalized only based on values of input signal of neural network (NN). Therefore in the result of normalization similar in shape input signals are transformed to close vectors, being inputs of NN.
Tomashevich D.S.
Scientific Center of Neurocomputers
国际会议
8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)
上海
英文
1024-1027
2001-11-14（万方平台首次上网日期，不代表论文的发表时间）