Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience
In this study, we offer a dual-decomposition hybrid time series forecasting model that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and variational mode decomposition (VMD) with gated recurrent unit (GRU) neural networks. By decomposing the non-stat...
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025024934 |
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| author | Aastha M. Sathe Supraja R. Aditya Antony Thomas |
| author_facet | Aastha M. Sathe Supraja R. Aditya Antony Thomas |
| author_sort | Aastha M. Sathe |
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| description | In this study, we offer a dual-decomposition hybrid time series forecasting model that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and variational mode decomposition (VMD) with gated recurrent unit (GRU) neural networks. By decomposing the non-stationary input time series with ICEEMDAN first and then VMD, we extract intrinsic mode functions (IMFs) and oscillatory modes, which enables our model to capture both linear and nonlinear patterns more effectively, thereby addressing the challenges posed by complex, non-stationary patterns in time series data, leading to improved forecasting accuracy via GRU. Validation on two real-world datasets shows that the proposed model achieves significant improvements over baseline and other pre-existing combination methods such as ICEEMDAN-RNN, ICEEMDAN-LSTM, ICEEMDAN-GRU, VMD-RNN, VMD-LSTM, VMD-GRU, ICEEMDAN-VMD-RNN, and ICEEMDAN-VMD-LSTM. This is evidenced by key performance metrics namely, RMSE, MAPE, MAE, R2 and quantile loss. A reliable methodology for increasing the accuracy of time series forecasting in real agricultural datasets, particularly, the fruits of UK, is provided by the ICEEMDAN-VMD-GRU framework. |
| format | Article |
| id | doaj-art-db5d125f6f8d44fb9018a61b9a171259 |
| institution | Matheson Library |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-db5d125f6f8d44fb9018a61b9a1712592025-08-02T04:47:41ZengElsevierResults in Engineering2590-12302025-09-0127106423Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilienceAastha M. Sathe0Supraja R.1Aditya Antony Thomas2Corresponding author.; Department of Mathematics, SAS, VITAP University, Amravathi, 522237, IndiaDepartment of Mathematics, SAS, VITAP University, Amravathi, 522237, IndiaDepartment of Mathematics, SAS, VITAP University, Amravathi, 522237, IndiaIn this study, we offer a dual-decomposition hybrid time series forecasting model that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and variational mode decomposition (VMD) with gated recurrent unit (GRU) neural networks. By decomposing the non-stationary input time series with ICEEMDAN first and then VMD, we extract intrinsic mode functions (IMFs) and oscillatory modes, which enables our model to capture both linear and nonlinear patterns more effectively, thereby addressing the challenges posed by complex, non-stationary patterns in time series data, leading to improved forecasting accuracy via GRU. Validation on two real-world datasets shows that the proposed model achieves significant improvements over baseline and other pre-existing combination methods such as ICEEMDAN-RNN, ICEEMDAN-LSTM, ICEEMDAN-GRU, VMD-RNN, VMD-LSTM, VMD-GRU, ICEEMDAN-VMD-RNN, and ICEEMDAN-VMD-LSTM. This is evidenced by key performance metrics namely, RMSE, MAPE, MAE, R2 and quantile loss. A reliable methodology for increasing the accuracy of time series forecasting in real agricultural datasets, particularly, the fruits of UK, is provided by the ICEEMDAN-VMD-GRU framework.http://www.sciencedirect.com/science/article/pii/S2590123025024934Non-stationary dataTime series forecastingICEEMDANVMDLSTMGRU: IMFs |
| spellingShingle | Aastha M. Sathe Supraja R. Aditya Antony Thomas Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience Results in Engineering Non-stationary data Time series forecasting ICEEMDAN VMD LSTM GRU: IMFs |
| title | Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience |
| title_full | Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience |
| title_fullStr | Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience |
| title_full_unstemmed | Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience |
| title_short | Enhancing agricultural sustainability: Time series forecasting with ICEEMDAN-VMD-GRU for economic-resilience |
| title_sort | enhancing agricultural sustainability time series forecasting with iceemdan vmd gru for economic resilience |
| topic | Non-stationary data Time series forecasting ICEEMDAN VMD LSTM GRU: IMFs |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025024934 |
| work_keys_str_mv | AT aasthamsathe enhancingagriculturalsustainabilitytimeseriesforecastingwithiceemdanvmdgruforeconomicresilience AT suprajar enhancingagriculturalsustainabilitytimeseriesforecastingwithiceemdanvmdgruforeconomicresilience AT adityaantonythomas enhancingagriculturalsustainabilitytimeseriesforecastingwithiceemdanvmdgruforeconomicresilience |