A Novel Time Series Forecasting Approach with Multi-Level Data Decomposing and Modeling

Time series produced in complex systems are always controlled by multi-level laws, including macroscopic and microscopic laws. These multi-level laws bring on the combination of long-memory effects and short-term irregular fluctuations in the same series. Traditional analysis and forecasting methods do not distinguish these multi-level influences and always make a single model for prediction, which has to introduce a lot of parameters to describe the characteristics of complex systems and results in the loss of efficiency or accuracy. This paper goes deep into the structure of series data, decomposes time series into several simpler ones with different smoothness, and then samples them with multi-scale sizes. After that, each time series is modeled and predicated respectively, and their results are integrated finally. The experimental results on the stock forecasting show that the method is effective and satisfying, even for the time series with large fluctuations