Application of empirical mode decomposition to improve deep learning for US GDP data forecasting

Shih-Lin Lin

doi:10.1016/j.heliyon.2022.e08748

Application of empirical mode decomposition to improve deep learning for US GDP data forecasting

Heliyon. 2022 Jan 12;8(1):e08748. doi: 10.1016/j.heliyon.2022.e08748. eCollection 2022 Jan.

Author

Shih-Lin Lin¹

Affiliation

¹ Graduate Institute of Vehicle Engineering, National Changhua University of Education, No.1, Jin-De Road, Changhua City, 50007, Taiwan.

Abstract

The application of deep learning methods to construct deep neural networks for the prediction of future econometric trends and econometric data has come to receive a lot of research attention. However, it has been found that the long short-term memory (LSTM) model is unstable and overly complex. It also lacks rules for handling econometric data, which can cause errors in prediction and in the actual data. This paper proposes an empirical mode decomposition (EMD) method designed to improve deep learning for understanding US GDP trends and US GDP data prediction research. The US GDP growth rate is used only for LSTM model prediction and for real data comparison; the root mean squared error (RMSE) is 2.7274. The US GDP growth rate is EMD decomposed to obtain the intrinsic mode functions (IMFs) after which the LSTM model is used to predict an RMSE of 0.93557.

Keywords: Deep learning; Econometrics data forecasting; Empirical mode decomposition.