出版社：Canadian Research & Development Center of Sciences and Cultures
摘要：Analog Complexing (AC) algorithm can be considered a sequential pattern recognition method for prediction. However, financial Time-series data are often nonlinear and non-stationary, which cause some difficulties when used AC algorithm in prediction. Aiming at this problem, in this paper, using Empirical Mode Decomposition (EMD) to handle original data, and we will obtain a series of stationary Intrinsic Mode Functions (IMF); then each IMF is predicted dynamically by AC. By the empirical studies on NYMEX Crude Oil Futures price show that AC algorithm based on EMD method have high precision in 1 step and 3 steps dynamically prediction. Key words: Analog Complexing algorithm, Empirical Mode Decomposition, Intrinsic Mode Function, Dynamically prediction
其他摘要：Analog Complexing (AC) algorithm can be considered a sequential pattern recognition method for prediction. However, financial Time-series data are often nonlinear and non-stationary, which cause some difficulties when used AC algorithm in prediction. Aiming at this problem, in this paper, using Empirical Mode Decomposition (EMD) to handle original data, and we will obtain a series of stationary Intrinsic Mode Functions (IMF); then each IMF is predicted dynamically by AC. By the empirical studies on NYMEX Crude Oil Futures price show that AC algorithm based on EMD method have high precision in 1 step and 3 steps dynamically prediction. Key words: Analog Complexing algorithm, Empirical Mode Decomposition, Intrinsic Mode Function, Dynamically prediction
