Ensemble empirical mode decomposition-based preprocessing method with Multi-LSTM for time series forecasting: a case study for hog prices

Abstract

Drastic hog price fluctuations have a great impact on the welfare of hog farmers, people's living standards, and the macroeconomy. To stabilise the hog price, hog price forecasting has become an increasingly hot issue in the research literature. Existing papers have neglected the benefits of decomposition and instead directly utilise models to predict hog prices by capturing raw data. Motivated by this issue, the authors introduce a new robust forecasting approach for hog prices that combines ensemble empirical mode decomposition (EEMD) and multilong short-term memory neural networks (Multi-LSTMs). First, EEMD decomposes the volatile raw sequence into several smoother subsequences. Second, the decomposed subsequences are predicted separately using a parallel structure model consisting of several LSTMs. Finally, the fuse function combines all the subresults to yield the final result. The empirical results suggest that the proposed method only has minor errors and proves the effectiveness and reliability in experiments on real datasets (2.55207, 4.816, and 0.332 on MAE, MAPE and RMSLE, respectively). Reliable forecasting of hog prices is beneficial to farmers and people to allow optimisation of their production and booking rates and to moderate the adverse effects of potential shocks.

KEYWORDS:

• Hog price forecasting
• EEMD
• LSTM
• machine learning
• deep learning

Acknowledgement

This research was supported by grants from the National Natural Science Foundation of China (No: 71963019) and the General Program for Public Visiting Scholars of the China Scholarship Council (CSC no: 202008360074). The authors would also like to extend special appreciation to the anonymous reviewers for their invaluable and professional suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.