New time series forecasting approach for complex systems based on series decomposing
Shen Hui-feng · Journal of Computer Applications · 2006
Time series are often produced in complex systems which are controlled both by macroscopic level and microscopic level laws, with long memory effect and short-term irregularly fluctuations coexisting in the same series. Traditional analysis and forecasting methods didn't distinguish these multi-level influences and always made a single model for predication, which had to introduce a lot of parameters to describe the characteristics of complex systems and result in the loss of efficiency and accuracy. However,we decomposed time series into several ones with different smoothness, all the sub-time series were respectively modeled and predicated with multi-scale sampling. Then the forecasting results of sub-time series were composed to get the result of the original time series. The experiment results on the stock forecasting show that the method is efficient, even for the time series with large fluctuations.