Quantum mechanics

Divide and conquer algorithms

Algorithm

Biology

Time series

Selection (genetic algorithm)

Data mining

Computer science

Bounded function

Paleontology

Physics

Pattern recognition (psychology)

Mathematics

Value (mathematics)

Mathematical analysis

Artificial intelligence

Series (stratigraphy)

Term (time)

Machine learning

Speech recognition

Subsequence

IEEE Access

To improve the prediction effect of time series, we make a systematic study of various time series prediction methods based on statistics and machine learning in this paper. In the experiment, we compare the prediction results of several prediction methods. In particular, much research has been done on the selection of experimental data because representative time series data can better test the effectiveness and practicability of the prediction method. Based on the idea of divide and conquer of complex problems and the strategy of continuous optimization of machine learning, we proposed the prediction methods of LSTM-TFE, LR-TFE, and BR-TFE combined the EEMD, LSTM, LR, and BR methods in this paper. These methods use EEMD to decompose complex time series into several relatively milder, more regular and stable subsequences. Then the prediction model of each subsequence based on machine learning is carried out by using the LSTM, LR, or BR methods. We use these prediction models to predict the value of each subsequence. Finally, the value of multiple subsequences is fused to form the prediction results of the original complex time series. To verify the proposed method comprehensively, we select three representative time series data to test this paper. From the experimental results, we found that the proposed method has a good effect.

Hybrid Method for Short-Term Time Series Forecasting Based on EEMD