Ensemble empirical mode decomposition-based preprocessing method with Multi-LSTM for time series forecasting: a case study for hog prices
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 de...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2111404 |
| _version_ | 1797683992525275136 |
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| author | Lianlian Fu Xinsheng Ding Yuehui Ding |
| author_facet | Lianlian Fu Xinsheng Ding Yuehui Ding |
| author_sort | Lianlian Fu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:23:55Z |
| format | Article |
| id | doaj.art-3cdd69ef44584019abae3e14921ac5a9 |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:23:55Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-3cdd69ef44584019abae3e14921ac5a92023-09-15T10:48:01ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013412177220010.1080/09540091.2022.21114042111404Ensemble empirical mode decomposition-based preprocessing method with Multi-LSTM for time series forecasting: a case study for hog pricesLianlian Fu0Xinsheng Ding1Yuehui Ding2Jiangxi Agricultural University ChinaJiangxi Agricultural University ChinaJiangxi Normal University ChinaDrastic 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.http://dx.doi.org/10.1080/09540091.2022.2111404hog price forecastingeemdlstmmachine learningdeep learning |
| spellingShingle | Lianlian Fu Xinsheng Ding Yuehui Ding Ensemble empirical mode decomposition-based preprocessing method with Multi-LSTM for time series forecasting: a case study for hog prices Connection Science hog price forecasting eemd lstm machine learning deep learning |
| title | Ensemble empirical mode decomposition-based preprocessing method with Multi-LSTM for time series forecasting: a case study for hog prices |
| title_full | Ensemble empirical mode decomposition-based preprocessing method with Multi-LSTM for time series forecasting: a case study for hog prices |
| title_fullStr | Ensemble empirical mode decomposition-based preprocessing method with Multi-LSTM for time series forecasting: a case study for hog prices |
| title_full_unstemmed | Ensemble empirical mode decomposition-based preprocessing method with Multi-LSTM for time series forecasting: a case study for hog prices |
| title_short | Ensemble empirical mode decomposition-based preprocessing method with Multi-LSTM for time series forecasting: a case study for hog prices |
| title_sort | ensemble empirical mode decomposition based preprocessing method with multi lstm for time series forecasting a case study for hog prices |
| topic | hog price forecasting eemd lstm machine learning deep learning |
| url | http://dx.doi.org/10.1080/09540091.2022.2111404 |
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