Effect Comparison on Forecasting of High-frequency Time Series by Non-linear Analysis

摘要

In order to obtain the predictability of high-frequency time series, this paper develops state space reconstruction and divergence calculation techniques have been for t+1 temporal trend of stock index, state space reconstruction technique preserve certain information on original time series which describes the asymptotic behavior of system. By describing the asymptotic behavior, the properties of the system will be shown better when the time t is large enough. Divergence calculation of dynamical system is used to describe the characterization of system and analyze the temporal trend. The divergence is locally equivalent to the trace of the Jacobian and measures the rate of the change of an infinitesimal state space volume V(t) following an orbit x(t). In this paper, we forecast the t+1 temporal of Shanghai stock market composite index based on 5- minute high-frequency time series and get a satisfying result.