基于经验模态分解的时间序列预测方法

时间序列预测方法广泛应用于各个领域。对非平稳非线性时间序列预测方法进行了研究,利用经验模态分解法将此类序列分解为平稳时间序列,然后选择合适的步长,应用机器学习算法对各个平稳子序列进行预测,各个子序列的预测值之和即为原序列的预测值。将该方法应用于楼宇等电能能耗数据,实验结果表明,基于经验模态分解方法的时间序列预测方法精度较高,适用于预测非线性非平稳时间序列。

The Method for Time Series Prediction Based on Empirical Mode Decomposition

Time series prediction methods were widely used in various fields.The prediction method for non-stationary and nonlinear time series was studied in this paper.This method decomposed such series into stationary time series using empirical mode decomposition method.And then an appropriate time-step was chosen and machine learning algorithm was applied to predict each stationary sub-sequence.The sum of predicted values was the forecasting results for the original sequence.The method was applied to electrical energy consumption dataset.The experimental results showed that the combined algorithm of machine learning and empirical mode decomposition method had higher accuracy and was suitable for predicting non-linear and non-stationary time series.