Economic Growth Prediction Algorithm of Coastal Area Based on Impulse Response Function
In order to solve the problems of low accuracy and long prediction time of traditional economic growth prediction algorithms in coastal areas, an algorithm based on impulse response function was designed to analyze economic growth prediction in coastal areas. Crawler technology is used to capture th...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Hindawi-Wiley
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/b8afa6ba56344b2bbe1c4173547cad0a |
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| Sumario: | In order to solve the problems of low accuracy and long prediction time of traditional economic growth prediction algorithms in coastal areas, an algorithm based on impulse response function was designed to analyze economic growth prediction in coastal areas. Crawler technology is used to capture the economic data of coastal areas and normalize the captured data. Based on the processed data, the impulse response function is used to analyze the relationship between different economic variables, so as to build the PSO-LSTM model, which is used to predict the economic growth trend of coastal areas. The experimental results show that, compared with the experimental comparison algorithm, the prediction accuracy of the algorithm designed in this paper is always above 97%, and the prediction time is always below 1 s, which has certain practical significance. |
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