Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model
Climate is a complex and chaotic system, and temperature prediction is a challenging problem. Accurate temperature prediction is also concerned in the fields of energy, environment, industry, and agriculture. In order to improve the accuracy of monthly mean temperature prediction and reduce the calc...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2020/8385416 |
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| author | Guohui Li Wanni Chang Hong Yang |
| author_facet | Guohui Li Wanni Chang Hong Yang |
| author_sort | Guohui Li |
| collection | DOAJ |
| description | Climate is a complex and chaotic system, and temperature prediction is a challenging problem. Accurate temperature prediction is also concerned in the fields of energy, environment, industry, and agriculture. In order to improve the accuracy of monthly mean temperature prediction and reduce the calculation scale of hybrid prediction process, a combined prediction model based on variational mode decomposition-differential symbolic entropy (VMD-DSE) and Volterra is proposed. Firstly, the original monthly mean meteorological temperature sequence is decomposed into finite mode components by VMD. The DSE is used to analyze the complexity and reconstruct the sequences. Then, the new sequence is reconstructed in phase space. The delay time and embedding dimension are determined by the mutual information method and G-P method, respectively. On this basis, the Volterra adaptive prediction model is established to modeling and predicting each component. Finally, the final predicted values are obtained by superimposing the predicted results. The monthly mean temperature data of Xianyang and Yan’an are used to verify the prediction performance of the proposed model. The experimental results show that the VMD-DSE-Volterra model shows better performance in the prediction of monthly mean temperature compared with other benchmark models in this paper. In addition, the combined forecasting model proposed in this paper can reduce the modeling time and improve the forecasting accuracy, so it is an effective forecasting model. |
| format | Article |
| id | doaj-art-8f3e89f78742480690133f6d50d700b7 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-8f3e89f78742480690133f6d50d700b72025-02-03T01:27:25ZengWileyAdvances in Meteorology1687-93091687-93172020-01-01202010.1155/2020/83854168385416Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive ModelGuohui Li0Wanni Chang1Hong Yang2School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaSchool of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaSchool of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, ChinaClimate is a complex and chaotic system, and temperature prediction is a challenging problem. Accurate temperature prediction is also concerned in the fields of energy, environment, industry, and agriculture. In order to improve the accuracy of monthly mean temperature prediction and reduce the calculation scale of hybrid prediction process, a combined prediction model based on variational mode decomposition-differential symbolic entropy (VMD-DSE) and Volterra is proposed. Firstly, the original monthly mean meteorological temperature sequence is decomposed into finite mode components by VMD. The DSE is used to analyze the complexity and reconstruct the sequences. Then, the new sequence is reconstructed in phase space. The delay time and embedding dimension are determined by the mutual information method and G-P method, respectively. On this basis, the Volterra adaptive prediction model is established to modeling and predicting each component. Finally, the final predicted values are obtained by superimposing the predicted results. The monthly mean temperature data of Xianyang and Yan’an are used to verify the prediction performance of the proposed model. The experimental results show that the VMD-DSE-Volterra model shows better performance in the prediction of monthly mean temperature compared with other benchmark models in this paper. In addition, the combined forecasting model proposed in this paper can reduce the modeling time and improve the forecasting accuracy, so it is an effective forecasting model.http://dx.doi.org/10.1155/2020/8385416 |
| spellingShingle | Guohui Li Wanni Chang Hong Yang Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model Advances in Meteorology |
| title | Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model |
| title_full | Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model |
| title_fullStr | Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model |
| title_full_unstemmed | Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model |
| title_short | Monthly Mean Meteorological Temperature Prediction Based on VMD-DSE and Volterra Adaptive Model |
| title_sort | monthly mean meteorological temperature prediction based on vmd dse and volterra adaptive model |
| url | http://dx.doi.org/10.1155/2020/8385416 |
| work_keys_str_mv | AT guohuili monthlymeanmeteorologicaltemperaturepredictionbasedonvmddseandvolterraadaptivemodel AT wannichang monthlymeanmeteorologicaltemperaturepredictionbasedonvmddseandvolterraadaptivemodel AT hongyang monthlymeanmeteorologicaltemperaturepredictionbasedonvmddseandvolterraadaptivemodel |