• Background:
In reality, time series is composed of several basic components,which have linear,nonlinear and non-stationary characteristics at the same time. Directly use a single model to modeling will show some limitations and the prediction accuracy is difficult to improve.
• Method:
We propose a mixed forecasting model based on time series decomposition,which named STL-EEMD-LSTM model. First, we use STL filtering algorithm to decompose the time series to obtain the trend component, seasonal component and the remainder component of the time series; then we use EEMD to decompose the seasonal component and the remainder component to obtain multiple sub-sequences.; After this, then we reconstruct the new seasonal component and the remainder component according to the fluctuation frequency of the sub-sequence.; Finally, we use LSTM to build a prediction model for each component obtained by decomposition.
• Results:
We applied the proposed model to simulation data and the time series of satellite calibration parameters and found that the hybrid prediction model proposed in this paper has high prediction accuracy.
• Conclusion:
Therefore, we believe that our proposed model is more suitable for the prediction of time series with complex components.
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